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"Identitas yang mana merupakan pengakuan atas diri seseorang, disahkan dan diakui negara melalui sistem pencatatan sipil dalam administrasi kependudukan, yang diatur dalam UU Adminduk, serta dituangkan dalam bentuk Nomor Induk Kependudukan (NIK) dan dokumen kependudukan. Melalui kebijakan pemanfaatan data kependudukan, Ditjen Dukcapil diposisikan sebagai penggerak atau katalisator dalam membentuk jaringan multiorganisasional untuk mendorong digunakannya NIK sebagai single identity number. Melalui konstruksi governance networks dalam pemanfaatan data kependudukan dimana berbagai sektor terhubung dengan Ditjen Dukcapil, pemanfaatan data menjadi pintu masuk bagaimana agar single identity number tersebut dapat terimplementasikan dengan baik. Namun dalam prosesnya, resistensi untuk melepaskan identitas sektoral dari lembaga lain masih tinggi. Hal ini menunjukkan bahwa lembaga lain belum memiliki urgensi atas perannya dalam governance networks untuk menggunakan NIK sebagai identitas dari instansinya. Sehingga diperlukan penguatan keterlibatan dan peran lembaga tersebut selaku pengguna pemanfaatan data kependudukan dalam konstruksi governance networks untuk dapat memperkuat governance networks antara Ditjen Kependudukan dan Pencatatan Sipil dengan pengguna pemanfaatan data kependudukan.

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Identity, which is an acknowledgment of a person's self, is legalized and recognized by the state through the civil registration system in population administration, which is regulated in the Administrative Law, and is set forth in the form of a population identification number that called Nomor Induk Kependudukan (NIK) and population documents. Through population data utilization policy, the Directorate General of Civil Registration and Population is positioned as a catalyst in forming a multi-organizational network to encourage the use of NIK as a single identity number. Through the construction of governance networks where various sectors are connected, population data utilization policy becomes the entry point for how the single identity number can be implemented properly. However, in the process, resistance to releasing sectoral identities from other institutions is still high. This shows that other institutions do not yet have the urgency of their role in governance networks to use the NIK as the identity of their agency. So it is necessary to strengthen the involvement and role of these institutions as users of population data utilization in the construction of governance networks to be able to strengthen governance networks between the Directorate General of Population and Civil Registration with users of population data utilization."

"Dalam melakukan penegakan hukum, seringkali personil Ditreksrimum PMJ membutuhkan keterangan data kependudukan untuk digunakan dalam proses penyelidikan dan penyidikan. Data kependudukan merupakan data yang diklasifikasikan sebagai data rahasia yang dikelola oleh Ditjen Dukcapil Kemendagri. Dengan klasifikasi data rahasia tersebut sehingga Ditreskrimum Polda Metro Jaya menemui kendala berupa panjangnya proses birokrasi yang ada antara Polri dan Kemendagri. Dengan menggunakan metode kualitatif dan pendekatan exploratory ideographics, penelitian ini bertujuan untuk menemukan kebenaran dan fakta serta analisis mekanisme penyediaan data kependudukan dalam rangka proses penegakan hukum di Direktorat Reskrimum Polda Metro Jaya serta faktor-faktor baik pendukung dan penghambat dalam penyediaan data kependudukan untuk penegakan hukum. Hasil temuan penelitian ini adalah proses penyediaan data kependudukan dalam penegakan hukum membutuhkan waktu yang lama sampai data kependudukan tersebut diberikan kepada penyelidik atau penyidik di Ditreskrimum PMJ. Oleh karena itu perlu diciptakan sebuah sistem atau SOP (Standar Operasional Prosedur) dalam konteks penggunaan data kependudukan dengan mengacu teknologi informasi dan komunikasi yang bisa digunakan sebagai wadah komunikasi dan koordinasi efektif antara personil Polri dengan personil Ditjen Dukcapil Kemendagri, sehingga terbentuk tata kelola keamanan informasi agar data kependudukan yang ada tidak disalahgunakan oleh orang yang tidak bertanggung jawab

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In carrying out law enforcement, the personnel of the Directorate of General Crimes of Jakarta Metropolitan Police Region often requires information on population data to be used in the inquiry and investigation processes. Population data is the data that is classified as confidential and managed by the Directorate General of Population and Civil Registry of Indonesian Ministry of Home Affairs. Due to its classification as confidential data, the Directorate of General Crimes of Jakarta Metropolitan Police Region has several difficulties in the form of lengthy bureaucratic processes between the Police Region and the Ministry of Home Affairs. The author employs the qualitative approach and explaratory ideographic method to find out the truth and facts as well as analyse the mechanism for providing population data in the context of law enforcement processes at the Directorate of General Crimes of Jakarta Metropolitan Police Region as well as both supporting and hindering factors in providing population data for law enforcement. The results of the study show that the processes of providing population data in the law enforcement takes a long time until the population data is given to junior investigators or investigators of the Directorate of General Crimes of Jakarta Metropolitan Police Region. Therefore, the author recommends the Police Region to create a system or SOP (Standard Operating Procedure) in the context of the use of population data with reference to information and communication technology that can be used as a forum for effective communication and coordination between police personnel and the personnel of the Directorate General of Population and Civil Registry of the Ministry of Home Affairs, so that the governance of information security is established and the existing population data is not misused by irresponsible persons."

"The UN Volume (Bogue, Arriaga and Anderton, l993)on population methodology has elaborated different methods forinterpolation of population data. There were two methods ininterpolating empirical data frequently discussed, that are mid-point method and cumulation-differencing method In some casesmid-point method was not recommended and cumulation-differencing method was recommended only on the basis of limiteddata. This study tries to explore other methods in manipulatingpopulation data for different nature of data. The paper when atone instance, finds that the method which was not recommended bythe said volume was acceptable using different nature of data. Inother case, the method which was recommended in the volume was_,round more sound in respect of other kind of data In addition,some new types of data have also been tried and appropriateinterpolation formulae were recommended."

"Penelitian ini bertujuan untuk menganalisis tingkat kepercayaan publik terhadap privasi dan keamanan data sehubungan dengan pengintegrasian Nomor Induk Kependudukan (NIK) dan Nomor Pokok Wajib Pajak (NPWP) di Indonesia. Implementasi integrasi ini diatur dalam Undang-Undang Nomor 7 Tahun 2021 tentang Harmonisasi Peraturan Perpajakan, yang bertujuan untuk meningkatkan efisiensi administrasi perpajakan dan kepatuhan wajib pajak. Namun, muncul kekhawatiran di kalangan masyarakat terkait potensi risiko pelanggaran privasi dan kebocoran data pribadi. Untuk melihat tingkat kepercayaan publik tersebut, penelitian ini menggunakan teori utama public trust menurut Grimmelikhuijsen dan Knies. Pendekatan penelitian yang digunakan dalam penelitian ini adalah pendekatan kuantitatif dengan teknik pengumpulan data melalui survei, wawancara mendalam, studi kepustakaan. Survei dilakukan secara online melalui platform Google formulir dengan hasil responden sebanyak 130 responden yang merupakan Wajib Pajak di Depok. Hasil penelitian ini menunjukkan bahwa mayoritas sampel masyarakat Kota Depok memiliki tingkat kepercayaan sedang mengenai pengintegrasian NIK-NPWP. Faktor utama yang mempengaruhi kepercayaan ini dipengaruhi dengan keyakinan masyarakat atas kemudahan dan kesederhanaan administrasi pajak melalui pengintegrasian NIK-NPWP ini. Namun masih terdapat tingkat kepercayaan yang rendah di antara responden terkait perlindungan privasi dan keamanan data karena kurangnya informasi transparan mengenai langkah-langkah keamanan yang diambil oleh pemerintah serta pengalaman negatif terkait insiden kebocoran data sebelumnya.

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This research aims to analyze the level of public trust in privacy and data security in connection with the integration of the Population Identification Number (NIK) and Taxpayer Identification Number (NPWP) in Indonesia. The implementation of this integration is regulated in Law Number 7 of 2021 concerning Harmonization of Tax Regulations, which aims to increase the efficiency of tax administration and taxpayer compliance. However, concerns have emerged among the public regarding the potential risk of privacy violations and personal data leaks. To see the level of public trust, this research uses the main theory of public trust according to Grimmelikhuijsen and Knies. The research approach used in this research is a quantitative approach with data collection techniques through surveys, in-depth interviews, literature studies. The survey was conducted online via the Google form platform with the results of 130 respondents who were Taxpayers in Depok. The results of this research show that the majority of the Depok City community sample has a moderate level of confidence regarding the integration of NIK-NPWP. The main factor influencing this trust is influenced by the public's belief in the ease and simplicity of tax administration through the integration of NIK-NPWP. However, there is still a low level of trust among respondents regarding privacy protection and data security due to the lack of transparent information regarding security measures taken by the government as well as negative experiences related to previous data leak incidents."

"Teknologi dalam kehidupan manusia telah berkembang pesat dan membawa banyak kenyamanan bagi orang-orang dalam berbagai aspek di kehidupan mereka. Selain itu, perkembangan teknologi membawa dampak berbahaya bagi lingkungan, terutama pada kualitas udara. Karena proses produksi di industri, jumlah konsentrasi polutan meningkat dengan cepat. Particulate matter halus (PM2.5) merupakan salah satu polutan berbahaya dan dianggap sebagai salah satu faktor utama penurunan kesehatan masyarakat. Banyak upaya yang sedang dilakukan untuk menyediakan pemantauan konsentrasi PM2.5. Peramalan PM2.5 disediakan untuk peringatan dini bagi orang-orang. Dalam hal peramalan, tingkat akurasi merupakan hal yang paling menantang. Model yang tepat perlu dibangun untuk memperroleh prediksi yang presisi. Saat ini, Deep Neural Network (DNN) adalah teknik kecerdasan buatan telah terbukti menyelesaikan beberapa permasalahan terkait prediksi. Oleh karena itu, tesis ini mengusulkan mekanisme optimisasi peramalan menggunakan kombinasi dari Golden Section Search dan Fruit Fly Optimization Algorithm dengan mekanisme pembersihan data (data cleaning) menggunakan model DNN. Mekanisme yang diusulkan terbukti secara efektif mengoptimalkan tiga model DNN yaitu Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) untuk mencapai akurasi perkiraan konsentrasi PM2.5 yang lebih baik

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Technology in human life has advanced tremendously and it brings a lot of convenient for people in various aspects of their life. Besides that, it also brings a harmful impact on the environment, especially on air quality. Due to industrial production, the quantity of pollutant concentration raises rapidly many times. Fine particulate matter (PM2.5), one of dangerous pollutant, is regarded as one of the main factors for the deterioration of public health. Many efforts were being created to provide the monitoring of PM2.5 concentrations. PM2.5 forecasting provided for early warning to people. In terms of forecasting, accuracy is the most challenging task. A proper model needs to be constructed to lead the precision prediction. Nowadays, Deep Neural Network (DNN) is an artificial intelligence technique that has proven to solve several prediction problems. Therefore, this thesis proposed the forecasting optimization mechanism employing the Golden Section Search and Fruit Fly Optimization Algorithm combines with a data cleansing mechanism using DNN models. The proposed mechanism effectively optimizes three DNN models that are Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) to achieve better forecasting accuracy of PM2.5 concentration"

"This Springerbreif introduces a threshold-based channel sparsification approach, and then, the sparsity is exploited for scalable channel training. Last but not least, this brief introduces two scalable cooperative signal detection algorithms in C-RANs. The authors wish to spur new research activities in the following important question: how to leverage the revolutionary architecture of C-RAN to attain unprecedented system capacity at an affordable cost and complexity.Cloud radio access network (C-RAN) is a novel mobile network architecture that has a lot of significance in future wireless networks like 5G. the high density of remote radio heads in C-RANs leads to severe scalability issues in terms of computational and implementation complexities. This Springerbrief undertakes a comprehensive study on scalable signal processing for C-RANs, where scalable means that the computational and implementation complexities do not grow rapidly with the network size."

"Aplikasi berbasis jaringan internernet real-time seperti VoIP dan Video Conferencing sangat sensitif terhadap gangguan berupa ketersediaan bandwidth, terjadinya delay, packet loss, dan jitter. Untuk jaringan data dengan trafik bervariasi (seperti voice, video, data), dibutuhkan perlakuan khusus terhadap trafik data tertentu. Implementasi teknologi Quality of Service u n t u k menguji kinerja umum dilakukan dan teknik yang paling populer adalah Differentiated of Service (DiffServ). Namun dengan meningkatnya penetrasi trafik data pada jaringan komunikasi, masalah skalabilitas dan efisiensi menjadi penting. Multi- Protocol Label Switching (MPLS) memberikan kecepatan, skalabilitas serta efisiensi terhadap unjuk kerja jaringan. Dengan mengintegrasikan kedua teknologi tersebut, diharapkan unjuk kerja jaringan semakin baik bagi aplikasi atau layanan yang memerlukan garansi Quality of Service (QoS). Dalam penelitian ini implementasi teknologi MPLS DiffServ-Aware tidak hanya dilakukan pada jaringan IPv4 namun juga pada jaringan IPv6.Simulasi menunjukkan aplikasi yang sensitif terhadap QoS seperti VoIP, implementasi MPLS/DiffServ mampu memberikan garansi QoS dengan delay mencapai 87 ms, packet loss sebesar 0% dan jitter yang sangat kecil. Untuk aplikasi video conferencing, unjuk kerja juga lebih baik jika dibandingkan dengan hanya menggunakan teknologi IP Best- Effort, DiffServ, maupun MPLS secara independen. Untuk jaringan IPv6, unjuk kerja throughput lebih baik dari IPv4, namun secara keseluruhan unjuk kerja jaringan IPv4 masih lebih baik dibandingkan jaringan IPv6.

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Real-time network based applications such as VoIP and Video Conferencing are critical when it deals with bandwidth, delay, packet loss, and jitter. In a service integrated network (such as voice,video, and data services), it is needed to treat a service accordingly. The implementation of Quality of Service are common in today's networks : one of those technologies is Differentiated of Service (DiffServ). Nevertheless, with higher penetration of data traffics from time to time, the issues of scalability and eficiency appear significantly. Multi-Protocol Label Switching (MPLS) offers a good solution for improving the network performance. By integrating those two technologies, it is expected to improve the overall network performance, especially for service or application that needs QoS-guaranteed. This research not only focus on the implementation of MPLS DiffServ- Aware in IPv4 networks environment but also in IPv6 as well.Simulation results, showed that MPLS/DiffServ technology provides best performance for real-time or QoS-sensitive applications. For VoIP application, the delay is recorded as 87 ms, packet loss 0% and a very low jitter. For video conferencing service, those QoS parameters are also better if compared to those technologies if are implemented separately. Althought IPv6 showed a higher throughput performance, still IPv4 showed a better performance generally."

"Universitas XYZ merupakan salah satu PTS dalam bentuk universitas yang berlokasi di Tangerang, yang memiliki kewajiban untuk menjamin dan memberikan pendidikan bermutu kepada mahasiswa selaku salah satu pemangku kepentingan yang memiliki dampak secara langsung pada mutu sebuah universitas. LP2MP bertugas untuk menyelenggarakan pengukuran dan pelaksanaan survei setiap semester dalam bentuk survei umpan balik. Hasil survei tersebut dapat dijadikan sebagai pedoman untuk perbaikan yang berkesinambungan untuk penyelenggaraan penjaminan mutu Dikti dan pengelolaan Universitas XYZ. Namun, pengolahan dan pengukuran data survei secara konvensional tidak cukup untuk mengeksplorasi informasi tersembunyi dari survei. EDM digunakan pada penelitian ini untuk mengolah dan menganalisa data dari Universitas XYZ berupa survei bagian Open Ended Question (OEQ) yang terdiri dari Student Feedback Questionaire (SFQ), Facility Satisfaction Questionaire (FSQ), dan Graduate Feedback Questionaire (GFQ).Tujuan dari penelitian ini adalah untuk mendukung pengambilan keputusan dalam mengambil tindakan proaktif terhadap perbaikan mutu Universitas XYZ. Penelitian ini melakukan klasifikasi label aspek, sentimen analisis, dan tren topik survei SFQ, FSQ, dan GFQ pada bagian OEQ. Klasifikasi label aspek Multi Class survei SFQ memilih model klasifikasi terbaik dengan membandingkan hasil evaluasi accuration, precision, recall, dan F1-Score terhadap setiap kombinasi fitur dan perbandingan empat algoritma klasifikasi yaitu Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbor (KNN), dan Support Vector Machine (SVM). Klasfikasi label aspek multi label survei FSQ dan GFQ memilih model klasifikasi terbaik dengan membandingkan hasil evaluasi tiga jenis library multilabel dari SciKit-Learn, yaitu Binary Relevance (BL), Label Power Set (LPS), dan Classifier Chain (CC) terhadap setiap kombinasi fitur dan empat algoritma klasifikasi tersebut.Hasil dari penelitian ini adalah teknik klasifikasi menggunakan kombinasi fitur TFIDF, Unigram, dan Bigram dengan algoritma SVM merupakan model klasifikasi terbaik untuk pelabelan aspek survei SFQ. Teknik klasifikasi menggunakan kombinasi fitur TFIDF, Unigram, dan Bigram dengan algoritma SVM dan library Multi Label CC merupakan model klasifikasi terbaik untuk pelabelan aspek survei FSQ. Teknik klasifikasi menggunakan kombinasi fitur Count Vectorizer, Unigram, dan Bigram dengan algoritma NB dan library Multi Label BR merupakan model klasifikasi terbaik untuk pelabelan aspek survei GFQ. Selain itu, algoritma SentiStrenghtID digunakan untuk mendapatkan sentimen dan algoritma LDA digunakan untuk mendapatkan tren topik tahunan pada setiap label aspek survei SFQ, FSQ, dan GFQ.

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XYZ University is one of the universities in the form of universities located in Tangerang, which has an obligation to guarantee and provide quality education to students as one of the stakeholders that has a direct impact on the quality of a university. LP2MP is tasked to carrying out measurements and implementation of feedback every semester in the form of surveys as one part of quality control directly to stakeholders. The results of the surveys can be used as a guideline for continuous improvement in the implementation of Dikti quality assurance and management of XYZ University. However, conventional processing and measurement of feedback data are not enough to explore hidden information from surveys data. EDM was used in this research to process and analyze data from XYZ University in the form of Student Feedback Questionaire (SFQ), Facility Satisfaction Questionnaire (FSQ), and the Graduate Feedback Questionaire (GFQ) in the Open-Ended Question (OEQ) section.

The purpose of the research is to support decision making in taking proactive actions towards improvement for self-evaluation and quality of XYZ University. This research carried out label aspect classification, analytical sentiment, and trends in the survey topics SFQ, FSQ, and GFQ in the OEQ section. Multi- class aspect label classification SFQ will choose the best classification model by comparing the results of the evaluation of accuracy, precision, recall, and F1-score for each feature combination and comparison of four classification algorithms namely Decision Tree (DT), Naïve Bayes (NB), K- Nearest Neighbor (KNN), and Support Vector Machine (SVM). The classification of the multi-label aspects of the FSQ and GFQ survey labels will have the best classification model by comparing the evaluation results of three multilabel library types from SciKit-Learn, namely Binary Relevance (BR), Label Power Set (LPS), and Classifier Chain (CC) to each combination of features and four classification algorithms.

The results of this research are Classification Techniques using a combination of features of TFIDF, Unigram, and Bigram with the SVM algorithm which is the best Multi Class classification model for labeling aspects of the SFQ survey. Classification techniques use a combination of TFIDF, Unigram, and Bigram features with the SVM algorithm and the Multi-Label library CC is the best Multi-Label classification model for labeling aspects of the FSQ survey. Classification techniques using a combination of Count Vectorizer, Unigram, and Bigram features with the NB algorithm and the Multi-Label library BR are the best Multi-Label classification models for labeling aspects of the GFQ survey. In addition, the SentiStrenghtID algorithm can be used to get sentiments and the LDA algorithm can be used to get annual topic trends on each survey aspect label SFQ, FSQ, and GFQ.

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