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"Bite-sized learning works in synergy with microlearning. It promotes small activities and content to understand the materials in a sequence of ways with meaningful information. Telegram, one social media, can be used as an online learning platform. Bite-sized learning encourages Telegram features in providing simple and fun activities comprehensively. Teachers shared knowledge and skills, organized teaching activities, and helped students practice through teacher talks, either in organizing a classroom or achieving the teaching goals. Since this study explored how bite-sized learning and teacher talk on Telegram in L2 Listening class, this study used a qualitative approach with a case study. The result revealed how well the teacher communicates with the students during online learning in giving instruction, sharing ideas and knowledge, stimulating students to respond to the teacher and material, cultivating students’ intellectual ability, and managing classroom activities. Furthermore, those activities need to be more noticed, extra patience and energy, a more creative in delivering instruction and materials to initiate and stimulate students to respond and engage in the class. These study results imply that during online learning, the teacher talk and bite-sized learning need to be implemented and improved well and creatively to promote students’ understanding, interaction and engagement."

"Penelitian ini membahas tentang membangun model machine learning pada aspek manusia dalam kesadaran keamanan informasi. Model dibangun melalui pendekatan classification dan clustering melalui proses secara garis besar meliputi: impor data, menangani data tidak lengkap, penyusunan dataset, feature scaling, membangun model serta mengevaluasi model. Dataset disusun berdasarkan hasil kuisioner yang merujuk The Human Aspects of Information Security Questionnaire pada masyarakat di Indonesia. Hasil model classification dievaluasi dengan beberapa metode yaitu analisa k-fold Cross Validation, Confusion Matrix, Receiver Operating Characteristic, serta perhitungan skor pada masing-masing model. Salah satu algoritma pada classification yang digunakan yaitu Support Vector Machine memiliki kinerja dengan akurasi 99,7% dan error rate sebesar 0,3%. Algoritma pada clustering salah satunya yaitu DBSCAN memiliki nilai adjusted rand index selalu mendekati nilai 0.

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This research discusses building a machine learning model on the human aspect of information security awareness. The model built through a classification and clustering approach through a broad outline process, including importing data, handling incomplete data, compiling datasets, feature scaling, building models, and evaluating models. Dataset arranged based on the results of a questionnaire that referred to The Human Aspects of Information Security Questionnaire to Indonesia society. The results of the classification model evaluated by several methods, namely k-fold Cross Validation analysis, Confusion Matrix, Receiver Operating Characteristics, and score calculation for each model. One of the algorithms for classification, the Support Vector Machine, has a performance with an accuracy of 99.7% and an error rate of 0.3%. One of the algorithms in clustering is that DBSCAN has an adjusted rand index value consistently close to 0."

"Tujuan utama dari penelitian ini adalah untuk mengkaji peran perubahan strategis terhadap kinerja perusahaan Ericsson melalui pembelajaran organisasi, yang mana dalam membentuk perubahan strategis yang kuat diperlukan kapabilitas pengetahuan TMT, komitmen organisasi dan kepemimpinan transformasional. Penelitian ini menggunakan pendekatan kuantitatif dan kualitatif. Data dikumpulkan dari 92 bisnis unit di 23 wilayah cabang Ericsson di Kawasan Asia & Australia. Penelitian ini menggunakan analisis Structural Equation Modeling ( SEM ), di mana hasil analisis tersebut menunjukkan bahwa terdapat hubungan positif yang signifikan antara kapabilitas pengetahuan TMT dan komitmen organisasi terhadap perubahan strategis. Perubahan strategis juga memiliki hubungan positif yang secara tidak langsung signifikan terhadap kinerja melalui pembelajaran organisasi dan juga perubahan strategis memiliki hubungan positif langsung yang signifikan terhadap kinerja. Namun kepemimpinan transformasional tidak memiliki pengaruh signifikan terhadap perubahan strategis. Beberapa faktor yang menghambat kepemimpinan transformasional di Ericsson antara lain keterbatasan otoritas wewenang dan perbedaan budaya antara pemimpin dengan wilayah kepemimpinan. Keterbatasan tersebut yang mendorong tidak optimalnya pembentukan perubahan strategis dan kinerja perusahaan melalui kepemimpinan transformasional yang dimiliki.

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The main objective of this research is to examine the role of strategic change on Ericsson's company performance through organizational learning, which in forming strong strategic change requires TMT knowledge capability, organizational commitment and transformational leadership. This study uses a quantitative and qualitative approach. Data is collected from 92 business units in Ericsson's 23 regional branches in the Asia & Australia Region. This study uses Structural Equation Modeling (SEM) analysis, where the results of the analysis show that there is a significant positive relationship between TMT knowledge capability and organizational commitment to strategic change. Strategic change also has a significant positive indirect relationship to performance through organizational learning and strategic change also has a significant direct positive relationship to performance. However, transformational leadership does not have a significant effect on strategic change. Some of the factors that hinder transformational leadership at Ericsson include limited authority and cultural differences between leaders and leadership areas. These limitations have led to the non-optimal formation of strategic changes and company performance through their transformational leadership."

"Pemelajaran mesin dikenal sangat berguna dalam menyelesaikan permasalahan prediksi dan klasifikasi melalui pembelajaran pola dan perilaku data yang tersedia. Oleh karena itu, pemelajaran mesin dapat dimanfaatkan di berbagai bidang kehidupan dan industri modern. Namun, kinerja pemelajaran mesin sangat tergantung dari model pemelajaran mesin yang digunakan maupun dari kualitas data yang digunakan untuk pemelajaran. Data yang tidak bersih, tidak representatif, dan ketersediaannya terbatas akan mengurangi kualitas hasil prediksinya.Penelitian ini bertujuan untuk menguji kombinasi beberapa metode pemrosesan data (yaitu MissForest, GAIN, ENN, dan TabGAN oversampling) dengan model pembelajaran mesin (yaitu model CatBoost dan model klasifikasi biner berbasis neural network) untuk memprediksi kasus mahasiswa putus studi di beberapa universitas di Indonesia menggunakan data dari PDDikti. Penambahan fitur dilakukan untuk memberi label bidang studi terhadap dataset tersebut. Selain penambahan fitur seleksi fitur relevan menggunakan korelasi Pearson serta feature importances juga dilakukan setelah pelatihan model awal. Google Colab dengan bahasa pemrograman Python digunakan untuk menjalankan algoritma pemrosesan data dan pelatihan model.Hasil penelitian menunjukkan bahwa model CatBoost dengan kombinasi metode imputasi GAIN, undersampling ENN, dan tanpa fitur kelompok bidang studi memberikan F1-score tertinggi yaitu 66,38% dengan nilai precision 71,75% dan nilai recall 61,76%. Apabila digunakan model klasifikasi biner pemelajaran dalam akan didapatkan metrik terbaik F1-score 62,32%. Hasil terbaik penelitian ini menunjukkan peningkatan F1-score sebesar 2,15% dibandingkan dengan F1-score pada penelitian sebelumnya yang menggunakan model CatBoost bersama kombinasi Missforest dan ENN tanpa fitur kelompokbidang studi. Penelitian ini menunjukkan bahwa oversampling dan undersampling memberikan dampak yang berlawanan terhadap metrik precision dan recall. Penelitian juga menemukan seleksi fitur dapat meningkatkan kinerja model namun tidak berdampak besar dibandingkan teknik-teknik lain misalnya balancing dan optimisasi hyperparameter.

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Machine learning is known to be very useful in solving prediction and classification problems

by learning the patterns and behavior of available data. Therefore, machine learning can be utilized in various areas of modern life and industry. However, the performance of machine learning is highly dependent on the machine learning model used as well as on the quality of the data used for learning. Data that is not clean, not representative, and scarce will reduce the quality of the prediction results.

This study aims to test the combination of several data processing methods (namely MissForest, GAIN, ENN, and TabGAN oversampling) with machine learning models (CatBoost and binary classification models based on neural networks) to predict dropout cases at several Indonesian universities using data from PDDikti. The addition of features is done to label data with their respective fields of study. Other than adding features, selection of relevant features using Pearson’s correlation as well as feature importances is also carried out after initial model training. Google Colab with the Python programming language is used to run data processing algorithms and train models.

This study shows that CatBoost with the combination of GAIN imputation, ENN undersampling, and no field of study feature results in the highest F1-score of 66.38%, which are composed of 71.75% in precision and 61.76% in recall. If a deep learning binary classification model is used instead, the best F1-score result is 62.32%. The best result from this study shows an increase in F1-score of 2.15% compared to the F1-score of the previous study (64.23%) which used CatBoost along with a combination of Missforest, ENN and no field of study features. This research shows oversampling and undersampling produce opposite effects on precision and recall scores. Research has also

found that feature selection can improve model performance but does not have a large impact compared to other techniques such as balancing and hyperparameter optimization"

"This Infoline defines action learning as a group effort that involves solving real world problems through the use of acquired learning and implementing systems-wide solutions. It discusses why this process works and the benefits to organizations that use this technique. It features brief case studies from Whirlpool, National Semiconductor, General Electric, and Cigna International Property and Casualty Corporation."

"Penggunaan aplikasi yang semakin beragam di perusahaan maupun organisasi menuntut adanya proses integrasi antar aplikasi. Hal ini dikarenakan untuk menjaga integritas dari data dan aplikasi. Salah satu metode pengintegrasian aplikasi pada perusahaan yang umum digunakan adalah dengan menggunakan Enterprises Service Bus (ESB). Minimnya pembahasan dan praktik mengenai penggunaan ESB pada perkuliahan Enterprise Application Integration (EAI) di Fasilkom UI membuat perlu adanya pengembangan learning environment penerapan ESB. Learning Environment ini dibuat dengan tujuan agar mahasiswa mendapatkan pengalaman dalam menggunakan ESB. Pada proses pengembangan turut dibandingkan dua buah open source software ESB yakni WSO2 ESB dan MuleSoft ESB. Perbandingan bertujuan untuk mengetahui ESB yang lebih sesuai untuk digunakan pada learning environment. Hasil perbandingan memaparkan kelebihan dan kekurangan masing-masing, serta kesesuaian ESB untuk digunakan pada learning environment.

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Various application usage in enterprise or organizational level strives for the need of integration between applications. This happened to keep data and application integrity in enterprises or organization. One from many approach for integrating application in enterprises is using Enterprise Service Bus (ESB). The minimum number of practice about ESB in Enterprise Application Integration (EAI) course at Faculty of Computer Science Universitas Indonesia makes the need of learning environment development for applying ESB. This learning environment aims to make students experienced with the usage of ESB. In the learning environment development process, there’s also benchmarking between two open source ESBs WSO2 ESB and Mulesoft ESB. This aims to know which ESB is more appropriate for the usage in learning environment. The results roll out the advantage and disadvantage of each ESB for the use in learning environment."

"Dari pengalaman saya dalam mempelajari Bahasa Inggris di Universitas saya, saya melihat banyak sifat dan kekhasan dalam bagaimana kelas mengaplikasikan pembelajaran Inggris. Tiap guru menggunakan approach pembelajaran dan pengajaran Bahasa Inggris secara berbeda, dengan banyak variasi dan tidak ada standar untuk semua kelas. Tiap approach berbeda dan tergantung terhadap sifat pengajar dan ketertarikan mahasiswa dalam topik kelas. Menurut saya, hal ini membuat proses pengajaran dan pembelajaran Bahasa Inggris menjadi tidak efektif. Harus ada study yang didedikasikan untuk mencari approach yang paling efektif untuk pembelajaran dan pengajaran Bahasa Inggris; yang sekaligus efektif dan bisa diterima oleh dosen dan mahasiswa. Untuk mencari solusi atas masalah ini, artikel ini mempresentasikan hasil literature review tentang approach pembelejaran dan pengajaran Bahasa Inggris yang efektif, serta hasil survei yang meneliti topik ini. Survey ini dilakukan dengan mengirimkan kuestioner ke mahasiswa dan dosen Program Sastra Inggris di Universitas X Depok. Hasil dari analisis menunjukkan konklusi yang baru dan menarik. Mahasiswa dan dosen tidak memiliki preferensi kuat untuk approach yang spesifik. Mereka lebih tertarik dengan approach campuran yang menggunakan elemen dari berbagai approach yang berbeda. Mereka juga ingin approach ini terbuka, tidak dibatasi oleh kurikulum yang ketat dan dapat beradaptasi berdasarkan kebutuhan dosen dan mahasiswa. Hasilnya, masih banyak perhatian yang perlu diberikan untuk meningkatkan standar kelas Bahasa Inggris supaya approach pembelajaran dan pengajaran yang baru dapat diimplementasikan secara efektif dan meluas.

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rom my experiences when learning English at my University, I noticed some peculiarities in how the class approach English learning. The way each teacher used English learning and teaching approaches was very different, with a lot of variations and no clear standard for each class. The approach used depended on individual teacher and the interest of learners on each topic. In my opinion, the different approaches that were used often resulted in learning and teaching processes that were not as effective as they should be. There needs to be a study dedicated on finding the most effective approach to English learning and teaching; one that is effective and well-received by both students and teachers. To find a solution to this problem, this paper presents the results of a literature review on effective English learning and teaching approaches and the results of a survey about this particular topic. The survey was conducted by sending a questionnaire to the students and teachers of the English Studies Program at University X in Depok. The results of the analysis show new & interesting conclusions. Students and teachers have no strong preference for any specific approaches. Instead, they are more interested in mixed approaches that have many elements from different approaches. They also want these approaches to be open, not limited to a rigid curriculum and are able to adapt based on students’ or teacher’s needs. More care and attention are needed to improve English classroom standard as a whole to make sure that these new approaches to learning & teaching are implemented in as many classes as possible."

"Menurut EEB Laboratory Jakarta, pada tahun 2016 sektor bangunan memiliki mengkonsumsi 18-20% dari total penggunaan energi di Indonesia, dan terus menerus meningkat seiring perkembangan teknologi yang membutuhkan sumber energi dalam upaya peningkatan kualitas hidup penghuni bangunan. Bangunan pintar merupakan sebuah konsep pemanfaatan teknologi yang tidak hanya bertujuan meningkatkan kenyamanan penghuni, tetapi juga dapat membantu dalam upaya efisiensi energi pada operasional bangunan. Maka dari itu, penelitian ini akan membantu upaya perancangan efisiensi energi pada sebuah bangunan dengan meninjau fitur dan karakteristik yang berpotensi dalam mendukung efisiensi energi dengan penerapan konsep bangunan pintar. Selain itu, akan dibuat sebuah model dengan pemanfaatan machine learning yang mampu memberikan prediksi tingkat penggunaan energi berdasarkan fitur-fitur yang diberikan. Model machine learning yang dihasilkan memiliki rata-rata nilai kesalahan relatif sebesar 17,76%, serta didapatkan tingkat efisiensi dengan penerapan seluruh fitur yang diidentifikasi pada rentang 34,5% hingga 45,3% tergantung pada lantai yang ditinjau.

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According to EEB Laboratory Jakarta, Indonesian building sector accounts for 18- 20% energy consumption in 2016, and this trend will continuously increase as technology needed to increase housing residents' quality keeps advancing. Smart building is a concept to utilise technology that does not only help increase occupants' comfort inside the building, but it can also help increase energy usage efficiency in building operations. This research aims to help the effort in designing energy efficiency planning for a building by reviewing potential features and characteristics that could help improves energy efficiency with implementation of the smart building concept. A model based on machine learning that could give prediction on the level of energy consumption based on given features will also be discussed here. This model of machine learning has a 17,76% average of relative error, as well as 34,5% until 45,3% efficienct level that includes implementation of all features, depending on analysed floor."

"Meskipun kajian mengenai bahan bakar dan penggerak alternatif sudah banyak, namun target dan aplikasinya dalam pengurangan emisi CO2 di pelabuhan masih kurang mendapat perhatian terutama di negara berkembang seperti Indonesia. Penelitian ini menggunakan machine learning dalam memperkirakan emisi CO2 dari aktivitas kapal di tujuh pelabuhan di Indonesia kemudian dicari variable yang berpengaruh pada total emisi sebagai fokus dalam pengembangan Ship Energy Efficiency Management Plan (SEEMP). Dilakukan prediksi total emisi CO2 menggunakan regresi hutan acak kemudian keefektifannya diverifikasi menggunakan validasi silang k-fold, hasil prediksi kemudian dibandingkan dengan total emisi perhitungan metode bottom-up. Hasil analisis attribute weight berdasarkan correlation menunjukkan bahwa daya mesin dan waktu operasi kapal di pelabuhan memiliki pengaruh yang lebih besar dalam menghasilkan emisi CO2. Prediksi total emisi menunjukkan bahwa model memiliki akurasi yang cukup rendah akibat banyaknya data yang kosong meskipun algoritma model sudah tergolong bagus. Akhirnya, operasi hemat bahan bakar dibahas dengan fokus pada tenaga dan bahan bakar alternatif serta peningkatan efisiensi kerja, penggunaan bahan bakar bersih dari hidrogen dan biofuel mamiliki potensi pengurangan yang paling tinggi dengan cold ironing sebagai alternatif yang dapat memenuhi syarat pengurangan emisi per tahun sebesar 20%. Dibutuhkan data yang lengkap untuk melakukan prediksi total emisi yang akurat serta pengembangan teknis dan ketersediaan sumber daya pada metode yang telah dibahas agar dapat di implementasikan kedalam Rencana Pengelolaan Efisiensi Energi Kapal.

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Although there are many studies on alternative fuels and drivers, the target and their application in reducing CO2 emissions at ports have received little attention, especially in developing countries such as Indonesia. This study uses machine learning to estimate CO2 emissions from ship activities at seven ports in Indonesia and then looks for variables that affect total emissions as a focus in developing a Ship Energy Efficiency Management Plan (SEEMP). Total CO2 emissions were predicted using random forest regression, their effectiveness was then verified using k-fold cross-validation, the prediction results were then compared with the total emissions calculated using the bottom-up method. The results of attribute weight analysis based on correlation show that engine power and ship operating time in port have a greater influence in producing CO2 emissions. Prediction of total emissions shows that the model has a fairly low accuracy due to the large number of blank data despite the model algorithm exelency. Finally, fuel-efficient operations are discussed with a focus on alternative power and fuels as well as improving work efficiency, the use of clean fuels from hydrogen and biofuels has the highest reduction potential with cold ironing as an alternative that can meet the requirements of 20% annual emission reduction. Complete data is needed to make accurate predictions of total emissions as well as technical development and resource availability on the methods discussed so that they can be implemented into the Ship Energy Efficiency Management Plan."