Abstract:Abstract: This study maps the vulnerability of landslides in Jayapura Regency, Indonesia, using a parameter weighting method within the framework of the Geographic Information System (GIS). The study identified key factors…
rs that contribute to landslide risk, including slope, soil type, rainfall, geology, and land use. The analysis revealed significant areas prone to landslides, with substantial portions classified as moderate to high risk. Comparison with the BNPB Inarisk method shows variations in the percentage of risk areas, highlighting the importance of the double assessment technique. The study underscores the need for an integrated, multidisciplinary approach to landslide risk management, emphasizing accurate data collection, land-use planning, and targeted mitigation strategies. These findings provide valuable insights for policymakers and disaster management agencies to minimize the impact of future landslides and promote sustainable development, especially in light of the 2019 landslide disaster in Sentani.
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Keywords: landslide; jayapura regency; parameter weighting method; inarisk BNPB methods; disaster mitigation.
Abstrak: Penelitian ini memetakan kerentanan tanah longsor di Kabupaten Jayapura, Indonesia, menggunakan metode pembobotan parameter dalam kerangka Sistem Informasi Geografis (GIS). Studi ini mengidentifikasi faktor-faktor kunci yang berkontribusi terhadap risiko tanah longsor, termasuk kemiringan, jenis tanah, curah hujan, geologi, dan penggunaan lahan. Analisis mengungkapkan area signifikan yang rentan terhadap tanah longsor, dengan porsi substansial diklasifikasikan sebagai risiko sedang hingga tinggi. Perbandingan dengan metode BNPB Inarisk menunjukkan variasi persentase area risiko, menyoroti pentingnya teknik penilaian ganda. Studi ini menggarisbawahi perlunya pendekatan multidisiplin yang terintegrasi untuk manajemen risiko tanah longsor, menekankan pengumpulan data yang akurat, perencanaan penggunaan lahan, dan strategi mitigasi yang ditargetkan. Temuan ini memberikan wawasan berharga bagi pembuat kebijakan dan lembaga penanggulangan bencana untuk meminimalkan dampak tanah longsor di masa depan dan mempromosikan pembangunan berkelanjutan, terutama mengingat bencana tanah longsor tahun 2019 di Sentani.
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Kata kunci: tanah longsor; kabupaten jayapura; metode pembobotan parameter; metode BNPB Inarisk; mitigasi bencana.
Abstract:Abstract: Bekasi Regency, being one of the key cities in Indonesia, offers a suitable setting to study the intricacies of marriage decision-making during a quarter-life crisis. This study focuses on the application of clustering…
ustering algorithms to categorize individuals based on their marriage choices. Data was collected from a questionnaire completed by 110 respondents from Bekasi Regency, specifically individuals aged 18 to 30 who are single, including 80 women and 30 men. Data analysis was conducted using the RapidMiner software to evaluate the effectiveness of three clustering algorithms K-Means, X-Means, and K-Medoids in categorizing marriage decision patterns among young people experiencing a Quarter Life Crisis in Bekasi Regency. Results indicate that each algorithm has its own strengths and limitations in handling Quarter Life Crisis data.The results of the analysis show that the K-medoids algorithm provides the best clustering results with the lowest DBI value of 0.195, followed by the X-Means algorithm with a value of 0.199 and K-Means with a value of 0.207. These results can help understand the pattern of marriage decisions in the Quarter Life Crisis phase and help provide insights for policymakers in Bekasi Regency to make more effective intervention programs.
Keywords: K-Means; K-Medoids; X-Means
Abstrak: Sebagai salah satu kota besar di Indonesia, Kabupaten Bekasi memberikan konteks yang tepat untuk mempelajari kompleksitas pengambilan keputusan pernikahan di tengah krisis seperempat usia. Penelitian ini berfokus pada pemanfaatan algoritma clustering untuk mengelompokkan individu berdasarkan pilihan pernikahan mereka. Data diambil dari kuesioner yang diisi oleh 110 responden di Kabupaten Bekasi, yang terdiri dari individu lajang berusia 18 hingga 30 tahun, yaitu 80 perempuan dan 30 laki-laki. Analisis data dilakukan dengan perangkat lunak RapidMiner untuk mengevaluasi efektivitas tiga algoritma pengelompokan—K-Means, X-Means, dan K-Medoids—dalam mengelompokkan pola keputusan pernikahan di kalangan pemuda yang menghadapi Quarter Life Crisis di Kabupaten Bekasi. Hasilnya menunjukkan bahwa setiap algoritma memiliki keunggulan dan kelemahannya masing-masing dalam memproses data Quarter Life Crisis. Hasil analisis menunjukkan bahwa algoritma K-medoids memberikan hasil clustering terbaik dengan nilai DBI terendah yaitu 0.195, diikuti oleh algoritma X-Means dengan nilai 0.199 dan K-Means dengan nilai 0.207. Hasil ini dapat membantu memahami pola keputusan menikah pada fase Quarter Life Crisis dan membantu memberikan wawasan bagi pembuat kebijakan di Kabupaten Bekasi membuat program intervensi yang lebih efektif.
Kata kunci: K-Means; K-Medoids; X-Means
Abstract:Abstract: Usability testing of online animal adoption systems is important because it is not only related to the comfort and safety of users, but also to the welfare of the adopted animals. So far, usability testing of the…
he animal adoption system on Eidos has not been carried out and there is a problem that there is confusion with the process flow in this animal adoption system. This usability testing uses the Heuristic Evaluation method and the System Usability Scale (SUS), which involves in-depth interviews to obtain results that are not only statistical assessments, but also deeper insights from the user's perspective. The results of the evaluation using Heuristic Evaluation obtained 59 problems, with an average severity rating of 3 or major, whose improvements have resulted in several draft design recommendations. The recommendation design has gone through final testing using the System Usability Scale (SUS) which managed to achieve a score of 78, or categorized as good, along with some minor critical feedback for system improvement. Therefore, users as a whole feel very satisfied in using the online adoption system from the results of the existing recommendation design. The implications of the resulting recommendation design have been tested for usability by three UI/UX design experts and users directly so that they can be used as a basis for developing a better online animal adoption system.
Keywords: animal adoption system; heuristic; prototype; system usability scale; usability
Abstrak: Pengujian usability sistem adopsi hewan secara online penting untuk diperhatikan karena tidak hanya berkaitan dengan kenyamanan dan keamanan penggunanya saja, tetapi juga meningkatkan kesejahteraan hewan yang diadopsi. Sejauh ini, pengujian usability dari sistem adopsi hewan pada Eidos masih belum dilakukan dan ditemukannya masalah bahwa adanya kebingungan dengan alur proses dalam sistem adopsi hewan ini. Pengujian usability ini menggunakan metode Heuristic Evaluation dan System Usability Scale (SUS), yang melibatkan in-depth interview untuk memperoleh hasil yang bukan hanya berupa penilaian statistik saja, melainkan juga wawasan yang lebih mendalam dari sudut pandang pengguna. Hasil evaluasi menggunakan Heuristic Evaluation mendapatkan 59 permasalahan, dengan rata-rata perhitungan severity rating sebesar tiga atau major, yang perbaikannya telah menghasilkan beberapa rancangan desain rekomendasi. Adapun desain rekomendasi tersebut telah melalui pengujian akhir menggunakan System Usability Scale (SUS) yang berhasil meraih skor sebesar 78, atau terkategori good, disertai beberapa umpan balik kritis yang bersifat minor untuk peningkatan sistem. Dengan demikian, pengguna secara keseluruhan merasa sangat puas dalam menggunakan sistem adopsi online dari hasil desain rekomendasi yang ada. Implikasi desain rekomendasi yang dihasilkan telah teruji kegunaannya oleh sebanyak tiga ahli UI/UX design dan pengguna secara langsung sehingga dapat digunakan sebagai landasan bagi pengembangan sistem adopsi hewan secara online yang lebih baik.
Kata kunci: heuristik; kebergunaan; prototipe; sistem adopsi hewan; system usability scale
Abstract:Abstract: Guntur Store is located on Jl. Sisingamangaraja, Kisaran, Asahan Regency. It focuses on selling basic needs, but the store faces a problem in its sales. The main issue encountered is the uncertainty in forecasting…
ing the stock of basic needs, which can lead to imbalance between supply and demand, due to Guntur Store still relying on manual ledger entries for reporting and inputting data into Microsoft Excel. This research aims to predict the demand for basic needs at Guntur Store using the Single Exponential Smoothing Method and to enhance prediction accuracy through the application of time series analysis techniques on historical sales data. The Single Exponential Smoothing Method will be employed to model and forecast patterns of demand for staple goods. The findings of this research can provide insights into future demand for goods, thereby improving supply chain optimization and ensuring adequate stock levels to effectively meet customer demands. The conclusion of this study is to obtain more accurate estimates in anticipating the evolving data patterns of basic needs at Guntur Store, thus providing valuable information for the store and policymakers involved in the distribution of basic needs.
Keywords: basic food needs; forecasting; single exponential smoothing
Abstrak : Toko Guntur terletak di Jl. Sisingamangaraja, Kisaran, Kabupaten Asahan. Berfokus pada penjualan kebutuhan sembako, namun dalam penjualan toko tersebut mengalami sebuah permasalahan. Permasalahan utama yang dihadapi adalah ketidakpastian dalam meramalkan kebutuhan stok Sembako yang dapat mengakibatkan ketidakseimbangan antara pasokan dan permintaan, dikarenkan Toko Guntur masih menggunakan buku besar dalam pembuatan laporan dan di input ke dalam microsoft excel. Penelitian ini bertujuan untuk memprediksi permintaan Sembako di Toko Guntur menggunakan Metode Single Exponential Smoothing serta meningkatkan akurasi prediksi melalui penerapan teknik analisis deret waktu terhadap data penjualan historis. Metode Single Exponential Smoothing akan digunakan untuk memodelkan dan memprediksi pola permintaan barang pokok. Hasil penelitian ini dapat memberikan jumlah permintaan barang di masa mendatang sehingga optimalisasi rantai pasok menjadi g lebih baik dan memastikan tingkat stok yang memadai untuk memenuhi permintaan pelanggan secara efektif. Kesimpulan penelitian ini untuk mendapatkan perkiraan yang lebih akurat dalam mengantisipasi pola data yang sedang berkembang kebutuhan sembako pada Toko Guntur sehingga menghasilkan informasi yang akurat dapat menjadi sumber informasi berharga bagi Toko Guntur, dan pembuat kebijakan yang terlibat dalam distribusi Sembako.
Kata Kunci: kebutuhan sembako; single exponential smoothing; peramalan
Abstract:Abstract: Recognizing one's interests and talents early on is crucial in guiding an individual toward a prosperous future. While distinct, interests and talents share a close relationship. Interest denotes a genuine attraction…
action to something without external pressure, and when consistently nurtured, it evolves into a skill or talent. Machine learning, specifically utilizing the SVM algorithm with the RBF kernel, can be applied to categorize interests and talents. Prior to SVM modeling, conducting Exploratory Data Analysis (EDA) is imperative for scrutinizing interests and talents. This analysis facilitates the identification of variables, enabling the elimination of missing values and ensuring the selection of appropriate interest and talent variables. The primary objective is to achieve optimal accuracy in modeling the classification of interests and talents. The insights gained from this research contribute to the creation of an application designed for categorizing interests and talents within SDN XYZ school. This application is designed for student use, assisting them in making informed decisions about their future education and career paths
Keywords: exploratory data analysis; interests and talents; machine learning; SVM Algorithm
Abstrak: Mengenali minat dan bakat seseorang sejak dini sangat penting dalam membimbing individu menuju masa depan yang sukses. Meskipun berbeda, minat dan bakat memiliki hubungan yang erat. Minat mengindikasikan ketertarikan yang tulus terhadap sesuatu tanpa tekanan eksternal, dan ketika terus-menerus dibina, berkembang menjadi keterampilan atau bakat. Pembelajaran mesin, khususnya dengan menggunakan algoritma SVM dan kernel RBF, dapat digunakan untuk mengelompokkan minat dan bakat. Sebelum pemodelan SVM, melakukan Analisis Data Eksploratif (EDA) sangat penting untuk mengkaji minat dan bakat. Analisis ini memfasilitasi identifikasi variabel, memungkinkan penghilangan nilai yang hilang, dan memastikan pemilihan variabel minat dan bakat yang tepat. Tujuan utamanya adalah mencapai akurasi optimal dalam pemodelan klasifikasi minat dan bakat. Temuan dari penelitian ini berkontribusi pada pengembangan aplikasi yang ditujukan untuk mengkategorikan minat dan bakat di sekolah SDN XYZ. Aplikasi ini dirancang untuk digunakan oleh siswa, membantu mereka membuat keputusan yang terinformasi mengenai pendidikan dan karier masa depan mereka.
Kata kunci: Algoritma SVM; exploratory data analysis; machine learning; minat dan bakat
Abstract:Abstract: Artificial Intelligence (AI) is currently gaining popularity across various industries, including healthcare, finance, and others. In this study, AI technology is employed to devise an optimal marketing strategy…
y for Code Verse Computer Accessories Store using the K-Means algorithm. As part of machine learning, the K-Means algorithm, categorized under unsupervised learning, is implemented to cluster sales data for computer accessory products over the last three months of 2023. The results of the K-Means analysis identify two main clusters. Cluster one (Cluster 1) comprises products such as Mouse, Keyboard, Monitor, Headset, and Speaker, indicating consistent purchasing patterns and high consumer interest. Recommendations are made to increase stock for Cluster 1. Meanwhile, Cluster two (Cluster 2) consists of Mic products with lower interest, and it is not advisable to increase stock. The implementation of K-Means provides insights into purchasing patterns, enabling Code Verse to develop more effective marketing and inventory management strategies.
Keywords: K-Means algorithm; artificial intelligence; clustering
Abstract: Kecerdasan Buatan (AI) kini meraih popularitas dalam berbagai industri, termasuk sektor kesehatan, keuangan, dan lainnya. Pada penelitian ini, teknologi AI digunakan untuk merancang strategi pemasaran optimal bagi Toko Aksesoris Komputer CodeVerse dengan menggunakan Algoritma K-Means. Sebagai bagian dari machine learning, Algoritma K-Means, yang termasuk dalam kategori unsupervised learning, diimplementasikan untuk mengelompokkan data penjualan produk selama tiga bulan terakhir tahun 2023. Hasil dari analisis K-Means mengidentifikasi dua cluster utama. Cluster pertama (Cluster 1) terdiri dari produk Mouse, Keyboard, Monitor, Headset, dan Speaker, menunjukkan pola pembelian yang konsisten dan tingginya minat konsumen. Rekomendasi untuk menambah stok diberikan. Sementara itu, Cluster kedua (Cluster 2) terdiri dari produk Mic dengan minat lebih rendah, dan tidak disarankan untuk menambah stok. Implementasi K-Means memberikan wawasan tentang pola pembelian, memungkinkan CodeVerse mengembangkan strategi pemasaran dan manajemen persediaan yang lebih efektif.
Keywords: Algoritma k-means; kecerdasan buatan; clustering
Abstract:Abstract: The habit of smoking is dangerous because of the addictive substances that make cigarettes addictive. Its addictive nature poses a significant risk, affecting personality with stress, depression and nervous disorders.…
orders. Body factors that indicate smoking include blood sugar levels, dental caries, and hemoglobin. To address this, research has been conducted with focused efforts to understand and address the risks associated with smoking and its impact on overall health. This research aims to choose the best method for predicting smokers by using feature selection techniques. The feature selection algorithms uses for that are Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Genetic Algorithm (GA) to select optimal attributes and uses the k-fold cross validation technique as the validation of the Artificial Neural Network algorithm. The data includes various parameters such as age, height, weight, vision, blood pressure, cholesterol, triglycerides, hemoglobin, AST, ALT, GTP, gender, dental caries and tartar. Hearing ability, urine protein content, and tartar were selected. The results showed that using the Analysis of Variance method showed higher accuracy (77.101%) compared to the Genetic Algorithm method (74.64%) and the Recursive Feature Elimination method (76.08%). Selection of relevant attributes increases the predictions and insights of the Artificial Neural Network model about the effects of smoking on health.
Keywords: artificial neural network; analysis of variance; genetic algorithm; recursive feature elimination; smoker prediction
Abstrak: Kebiasaan merokok berbahaya karena adanya zat adiktif yang membuat rokok menjadi ketagihan. Sifatnya yang membuat ketagihan menimbulkan risiko yang signifikan, mempengaruhi kepribadian dengan stres, depresi, dan gangguan saraf. Faktor tubuh yang mengindikasikan kebiasaan merokok antara lain kadar gula darah, karies gigi, dan hemoglobin. Untuk mengatasi hal ini, penelitian telah dilakukan dengan upaya terfokus untuk memahami dan mengatasi risiko yang terkait dengan merokok dan dampaknya terhadap kesehatan secara keseluruhan. Penelitian ini bertujuan untuk memilih metode terbaik dalam memprediksi perokok dengan menggunakan teknik seleksi fitur. Metode seleksi fitur yang digunakan adalah Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), dan Genetic Algorithm (GA) untuk memilih atribut yang optimal dan menggunakan teknik k-fold cross validation sebagai validasi algoritma Artificial Neural Network. Data tersebut mencakup berbagai parameter seperti umur, tinggi badan, berat badan, penglihatan, tekanan darah, kolesterol, trigliserida, hemoglobin, AST, ALT, GTP, jenis kelamin, karies gigi dan karang gigi. Kemampuan pendengaran, kandungan protein urin, dan karang gigi dipilih. Hasil penelitian menunjukkan bahwa penggunaan metode Analysis of Variance menunjukkan akurasi yang lebih tinggi (77,101%) dibandingkan dengan metode Genetic Algorithm (74,64%) dan metode Recursive Feature Elimination (76,08%). Pemilihan atribut yang relevan meningkatkan prediksi dan wawasan model Jaringan Syaraf Tiruan tentang dampak merokok terhadap kesehatan.
Kata kunci: artificial neural network; analysis of variance; genetic algorithm; prediksi perokok; recursive feature elimination
Abstract:Abstract: Twitter, as a social media platform, has rapidly grown as a means for people to express their opinions and thoughts on various topics, including education. The number of Twitter users surged to 10.645.000 in 2020,…
20, with a significant increase during the pandemic. Telkom University, as a private institution of higher education in Indonesia, has become one of the topics of discussion on Twitter. Users’ opinions about Telkom University vary, ranging from positive to negative. To gain deeper insights into public view, sentiment analysis is essential. The analysis follows the Knowledge Discovery in Databases (KDD) process, utilizing the Naive Bayes classification algorithm. The evaluation results indicate the best accuracy achieved with an 80:20 data split, resulting in an accuracy rate of 82.05%, precision of 82.3%, recall of 82.05%, and F1-Score of 82.08%. The Naïve Bayes model demonstrates good performance for sentiment analysis of public views regarding Telkom University on Twitter.
Keywords: naïve bayes; sentiment analysis; twitter; telkom university.
Abstrak: Media sosial Twitter berkembang pesat sebagai sarana masyarakat berekspresi untuk menuangkan opini dan pikiran mereka mengenai topik apapun, termasuk pendidikan. Pengguna Twitter meningkat tajam hingga 10.645.00 pengguna pada tahun 2020 dan terus meningkat selama pandemi. Telkom University sebagai perguruan tinggi menjadi salah satu topik yang dibicarakan yang berkaitan dengan pendidikan. Pendapat mengenai Telkom University yang diungkapkan oleh pengguna Twitter beragam, baik positif maupun negatif. Analisis sentimen diperlukan untuk memahami pandangan publik lebih mendalam. Digunakan tahapan Knowledge Discovery in Databases dan algoritma klasifikasi Naïve Bayes dalam analisis ini. Hasil evaluasi menunjukkan akurasi paling baik dicapai dengan rasio data 80:20, dengan nilai akurasi sebesar 82.05%, nilai presisi sebesar 82.3%, nilai recall sebesar 82.05%, dan nilai F1-Score sebesar 82.08%. Model klasifikasi Naïve Bayes memiliki performa baik untuk analisis sentimen pandangan publik di Twitter mengenai Telkom University.
Kata kunci: analisis sentimen; naïve bayes; twitter; telkom university.
Abstract:This study aims to construct an integrated constellation model and determine optimal strategies for reinforcing lecturers’ professional commitment at leading private universities in Bogor. Utilizing the POP-SDM (Modeling…
ng and Optimization of Management Resources) framework, the research integrates elements of local wisdom and organizational support within a systemic approach to human resource development. The exploratory qualitative phase involved in-depth interviews and focus group discussions to uncover major determinants influencing professional commitment. From the thematic analysis, four principal variables emerged—teamwork, organizational climate, religiosity, and work motivation—which were validated through expert judgment. Quantitative verification was subsequently performed using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) technique to examine both direct and indirect relationships among constructs. The analysis confirmed that all variables exert positive and significant effects on lecturers’ professional commitment, with work motivation being the most dominant factor. To refine improvement priorities, the SITOREM (Scientific Identification Theory for Conducting Operational Research in Educational Management) method was applied, identifying indicators that should be improved, maintained, or further developed. The findings offer empirical and practical insights, including: (1) a validated POP-SDM–based commitment model combining cultural and organizational dimensions; (2) evidence-based strategies to enhance lecturer professionalism; and (3) an optimization framework to guide sustainable lecturer development in higher education institutions.
Abstract:The increasing complexity and dynamics of the business environment at local, regional, and global levels have driven PT PLN (Persero)’s Internal Audit Unit (SPI) to adopt a Purpose-Driven Internal Audit approach, positioning…
ioning the audit function as a strategic partner for long-term organizational success. However, the Internal Stakeholder Satisfaction Index for assurance services in the Construction, Generation, and New Renewable Energy Audit Division (AKP) fell short of its targets in 2023 and 2024. This study aims to analyze the impact of audit service quality—comprising tangibles, reliability, responsiveness, assurance, and empathy—on auditee satisfaction. Using a quantitative approach with PLS-SEM and Importance–Performance Map Analysis (IPMA), data were collected from AKP Division auditees in 2024. The results show that all dimensions of audit service quality positively and significantly influence satisfaction, with responsiveness and empathy being the most dominant factors. The findings offer practical insights for SPI PLN in prioritizing service improvements based on user perceptions and provide theoretical support for the SERVQUAL model as a key psychological mechanism in fostering satisfaction within trust-based professional services