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All Journal ComEngApp : Computer Engineering and Applications Journal JSI: Jurnal Sistem Informasi (E-Journal) Annual Research Seminar CESS (Journal of Computer Engineering, System and Science) Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jiko (Jurnal Informatika dan komputer) Informatik : Jurnal Ilmu Komputer Data Science: Journal of Computing and Applied Informatics Building of Informatics, Technology and Science Jurnal Generic Malcom: Indonesian Journal of Machine Learning and Computer Science ABDIKOM
Ahmad Rifai
Universitas Sriwijaya
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Other

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PENGEMBANGAN SISTEM INFORMASI MANAJEMEN DATA KEPENDUDUKAN DESA GELEBAK DALAM KECAMATAN RAMBUTAN KABUPATEN BANYUASIN Sutarno Sutarno; Kemahyanto Exaudi; Rossi Passarella; Ahmad Rifai; Huda Ubaya; Dedy Kurniawan; Rahmad Fadli Isnanto; Purwita Sari
Jurnal Abdikom Vol 1 No 1 (2022): JURNAL ABDIKOM
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Desa Gelebak Dalam memiliki luas wilayah 17.779 Ha dengan jumlah penduduk ± 2.100 jiwa dari 552 kepala keluarga. Pekerjaan utama masyarakat desa adalah bertani sawah. Selain itu budidaya ikan sungai dan perkebunan seperti jagung dan buah naga juga dilakukan oleh masyarakat setempat. Informasi ini diperoleh berdasarkan penjelasan perangkat desa tanpa bukti tertulis. Hal ini menyebabkan perangkat desa sulit untuk memetakan potensi sumber daya yang ada dan berakibat pada kurang tepatnya bantuan, penyuluhan dan pendampingan yang diberikan pemerintah maupun swasta untuk mengembangkan potensi desa. Tujuan pengabdian ini adalah menerapkan sistem e-government pedesaan yang dapat membantu perangkat desa dalam mengelola informasi yang berkaitan dengan administrasi kependudukan, surat-menyurat, data sumber daya dan potensi desa, serta pelaporan informasi yang dapat membantu pemerintah mengambil keputusan dalam perencanaan pengembangan desa. Tahapan pelaksanaan kegiatan ini menggunakan metode FAST yaitu survei dan diskusi di desa, analisa permasalahan dan kebutuhan desa, menentukan model bisnis sistem, analisa kebutuhan sistem, serta pengembangan dan implementasi sistem. Hasil akhir dari kegiatan pengabdian ini adalah masyarakat desa dapat dengan mudah dan cepat memproleh informasi secara transparansi. Potensi desa dapat ditingkatkan dengan penyuluhan yang tepat sasaran melalui rekomendasi data sistem. Perangkat desa dapat mengelola dan menemukan data kependudukan dengan mudah melalui sistem database yang terpusat.
Sentiment and Topic Analysis of Digital Community Application Gamer Reviews using SVM-LDA and CRISP-DM Muhammad Mayda Ary Pratama; Dedy Kurniawan; Ahmad Rifai; Ken Ditha Tania
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5746

Abstract

Impatient behavior among gamers is often reflected in sharp and emotionally charged digital reviews, particularly in the use of community applications such as Discord. This study explores expressions of impatience through sentiment and topic analysis. By adopting the CRISP-DM framework, a total of 10,000 Indonesian-language reviews collected from the Google Play Store were analyzed. The analytical process begins with sentiment labeling using IndoBERT, followed by polarity classification using the Support Vector Machine (SVM) algorithm, and topic exploration through the Latent Dirichlet Allocation (LDA) method. The results indicate that 57.4% of the reviews express positive sentiment, primarily related to voice communication quality and community interaction features. In contrast, 42.6% of the negative reviews commonly convey frustration regarding login issues and verification processes. The SVM model optimized using Bayesian Optimization achieved an accuracy of 90.46%. This study highlights that Discord serves not only as a communication platform but also as a reflection of users’ high expectations for system speed and stability. The main contribution of this research lies in the integration of SVM–LDA methods within the CRISP-DM framework to better understand the digital behavior of Indonesian gamers. The practical implications of these findings provide strategic insights for developers to improve authentication reliability and community features in alignment with user characteristics.
Comparison of Clustering Algorithms for Analyzing the Impact of Conflict on Poverty and Inflation M Raykah Alam Ramadan; Dhio Pratama Wiransyah; Satria Ramadhani; Rayya Ramadhan Simangunsong; Ken Dhita Tania; Alsella Meiriza; Ahmad Rifai
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9512

Abstract

Armed conflict can have significant impacts on the social and economic conditions of a region, particularly on poverty levels and inflation. This study aims to analyze the impact of conflict on key economic indicators using a Knowledge Management System (KMS) approach and to compare the performance of clustering algorithms in identifying underlying data patterns. The research applies clustering analysis by comparing K-Means, DBSCAN, and Hierarchical Clustering algorithms to group data based on similarities in economic characteristics. The dataset used in this study consists of several indicators, including poverty levels before and during conflict, extreme poverty rates, inflation rates, GDP changes, and currency devaluation. Data preprocessing techniques such as normalization are applied to ensure comparability among variables. The evaluation of clustering performance is conducted using Silhouette Score and Davies–Bouldin Index to determine the most effective algorithm. The results show that clustering methods are able to identify distinct grouping patterns of regions based on the level of conflict impact on economic conditions. Among the evaluated algorithms, DBSCAN demonstrates superior performance in handling complex and uneven data distributions. The analysis also indicates a consistent tendency for poverty and inflation to increase during periods of conflict, highlighting the economic vulnerability of affected regions. Furthermore, the integration of clustering results into a Knowledge Management System enables the transformation of analytical outputs into structured knowledge that can support data-driven decision making. These findings are expected to contribute to the development of more effective economic policies and analytical frameworks in conflict-affected areas.
Hybrid Machine Learning for Knowledge Discovery in E-Commerce Reviews Jeremiah Alwin Siahaan; Lailla Syal Syabilla; M. Thoriqul Fadli; Mei Intan Natasyah; Allsela Meiriza; Ken Ditha Tania; Ahmad Rifai
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12635

Abstract

The rapid growth of e-commerce platforms like Tokopedia has triggered a massive accumulation of over 65,000 customer reviews, yet it is often accompanied by information pollution in the form of non-informative reviews that hinder consumer decision-making processes. This research aims to extract new knowledge regarding these review characteristics through the implementation of the Knowledge Discovery in Database (KDD) framework, integrating a hybrid K-Means Clustering and Random Forest algorithm. Diverging from conventional classification approaches, this study utilizes K-Means as an exploratory instrument to naturally map six latent topic patterns of reviews based on their textual structure. Experiments were conducted on 35,000 data samples using TF-IDF features enriched by cluster labels as structural predictors. The results indicate that the hybrid model achieves 94.41% accuracy with an F1-score of 0.90 for the non-informative class, showing high stability via 5-Fold Cross-Validation (94.56% ± 0.19%) . The most crucial knowledge discovery is evidenced through SHAP analysis, where the cluster feature ranks 7th out of 1,001 predictor features, confirming that semantic grouping provides a richer structural context than pure lexical features . Furthermore, error analysis reveals specific linguistic challenges such as sarcasm and semantic ambiguity as constraints in automated review detection . This research provides a managerial contribution to e-commerce platforms in enhancing information quality and mitigating information overload issues.
Comparative Analysis of the Performance of Machine Learning Methods and Text Embedding Techniques in Classifying Toxic Conversations in the Roblox Game Octa Dama Yanti; Syifa Alfariani; Syifa Naura Milla Celesta; Ken Dhita Tania; Ahmad Rifai
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12646

Abstract

Online games have evolved into digital social spaces where player interactions often include toxic communication, potentially affecting user experience and psychological well-being, especially among younger players. This research is intended to examine and compare the performance of various machine learning algorithms in classifying toxic chat on the Roblox platform and to identify underlying linguistic patterns. The dataset consists of 7,119 Indonesian-language chat data labeled into six categories: identity_hate, insult, obscene, severe_toxic, threat, and toxic. The methodology includes data preprocessing, text representation using Bag-of-Words (BoW) and TF-IDF, and classification using Naive Bayes, Support Vector Machine (SVM), and Random Forest. To assess how well the model performs, several metrics are used, including accuracy, precision, recall, F1-score, and 3-fold cross-validation. The results show that SVM with TF-IDF achieves the best performance with 84.48% accuracy, followed closely by SVM with BoW. The findings indicate that while classical machine learning models remain effective, challenges persist in distinguishing linguistically similar categories.
Knowledge Discovery of AI Usage Dependency Patterns in Learning Activities Using Random Forest, XGBoost, Logistic Regression with SHAP-Based Interpretation Fidela Tertia Alfino; Puti Chalisa Wardhana; A. Salwa Aurelya Putri; Athiyyah Nuha Rotifa; Ken Ditha Tania; Ahmad Rifai; Dedy Kurniawan
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12745

Abstract

The increasing use of Artificial Intelligence (AI) in education has influenced various learning activities. However, excessive AI usage has the potential to create dependency patterns that may affect students’ learning independence and critical thinking abilities. This study aims to analyze patterns of AI usage dependency in learning activities using a machine learning approach and to interpret the factors influencing such dependency. The analysis was conducted using a publicly available dataset representing usage intensity, session duration, AI assistance level, repeated usage behavior, and students’ academic characteristics. The research stages consisted of data preprocessing, categorical variable encoding, feature engineering, the construction of the Knowledge Dependency Level variable, class imbalance handling using SMOTE, and model evaluation using Stratified 5-Fold Cross Validation. The dataset was divided into 80% training data and 20% testing data, then modeled using Logistic Regression, Random Forest, and XGBoost. The results showed that XGBoost achieved the best performance with an accuracy of 0.6845, precision of 0.7288, recall of 0.6845, F1-score of 0.7028, and an AUC value of 0.860, indicating better discrimination capability compared to Random Forest and Logistic Regression. To support the knowledge discovery process, an interpretative analysis using SHAP was conducted to identify the contribution of each feature to the classification results. The interpretation revealed that SatisfactionRating was the most dominant feature influencing the prediction of AI usage dependency levels, followed by FinalOutcome, while academic factors such as StudentLevel and Discipline contributed relatively less. These findings transform previously implicit AI usage behavior patterns into explicit knowledge.
ANALYSIS OF SEISMICITY ANOMALIES IN SULAWESI USING DBSCAN BASED ON USGS DATA (2021–2026) Muhammad Dzaky Hasyim; Muhammad Wahyu Hikmalsyah; Gabriel Sebastian Santoso; Ken Ditha Tania; Allsela Meiriza; Ahmad Rifai
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 2 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i2.11728

Abstract

Sulawesi is one of Indonesia's most tectonically complex regions, situated at the meeting point of major plates and active fault systems, which results in significant seismic activity. This study aims to analyze seismicity anomalies in Sulawesi from 2021 to 2026 using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm applied to United States Geological Survey (USGS) data. The methodology involves a hybrid approach for parameter optimization, utilizing both the K-Distance Graph (KneeLocator) and Grid Search to ensure results are geologically representative. From an initial dataset of 859 events, 839 earthquake records were processed after geographic filtering, showing an average magnitude of 4.80 and an average depth of 70.15 km. The analysis reveals that the optimal parameters (ε = 0.2897 and MinPts = 3) produced 25 distinct clusters that align with real geological structures such as the Palu-Koro Fault and the Molucca Sea subduction zone. The algorithm successfully identified 66 points (7.87%) as seismic anomalies or noise, representing sporadic tectonic activity outside primary density zones. Furthermore, Convex Hull visualization identified a significant "seismic gap" between the Palu-Koro segment and Southeast Sulawesi, indicating a high-risk zone for potential future energy release. These findings demonstrate that density-based clustering is highly effective for mapping seismic hazards in complex tectonic regions, providing vital data for sustainable disaster mitigation planning in Sulawesi.
Perancangan Sistem Pengatur pH Air Akuarium Menggunakan Kendali Logika Fuzzy Sarmayanta Sembiring; Ahmad Rifai; Sutarno Sutarno; Pascal Adhi Kurnia Tarigan
Informatik : Jurnal Ilmu Komputer Vol 16 No 1 (2020): April 2020
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1085.536 KB) | DOI: 10.52958/iftk.v16i1.1682

Abstract

Telah dirancang sebuah sistem pengatur pH air akuarium dengan kendali logika fuzzy. Sistem ini akan menambahkan air basa/asam dengan volume sesuai dengan output  sistem kendali  yang di alirkan ke akuarium secara otomatis untuk mencapai pH air akuarium yang telah ditargetkan.  Input sistem ini adalah pH air penambah basa/asam yang di inputkan melalui keypad 4x4, pH air akuarium yang di diteksi sensor pH, volume air akuarium yang di diteksi menggunkan sensor ultrasonik. Output sistem ini adalah penambahan air basa/asam ke dalam akuarium dengan menggunakan pompa DC dan volumenya terukur menggunakan rangkaian sensor batas air dan  water flow sensor untuk memastikan volume air penambah telah sesuai dengan output sistem kendali. Hasil eksperimen menunjukkan sistem yang dirancang telah dapat berjalan dengan baik, dimana sensor ultrasonik telah dapat menditeksi jarak sebagai variabel untuk mencari volume air akuarium dengan error rata-rata 1,875%, sensor pH telah dapat menditeksi pH air akuarium dengan error rata-rata 1,76%, sistem pengatur air penambah basa dapat mengalirkan air sesuai dengan volume yang di inginkan dengan error rata-rata 3,56%,  sistem pengatur air penambah asam dapat mengalirkan air sesuai dengan volume yang di inginkan dengan error rata-rata 7,56%, Sistem kendali fuzzy sebagai pengatur pH air akuarium telah berjalan dengan baik dengan menurunkan error rata-rata dari 5,34% menjadi 1,59%.
Perancangan Sistem Pengatur Electrical Conductivity (EC) Air Menggunakan Kendali Logika Fuzzy Ahmad Rifai; Sarmayanta Sembiring; Al Farissi Al Farissi; Donny Giovanna Karo Karo
Informatik : Jurnal Ilmu Komputer Vol 16 No 1 (2020): April 2020
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1296.09 KB) | DOI: 10.52958/iftk.v16i1.1683

Abstract

Telah dirancang sebuah sistem pengatur electrical Conductivity (EC) air dengan menggunakan kendali logika fuzzy. Sistem pengatur ini berfungsi untuk memutuskan banyaknya volume air dengan EC rendah/tinggi yang harus ditambahkan untuk mencapai EC air pada penampungan sesuai dengan target yang di inputkan. Penambahan air dengan EC rendah/tinggi dilakukan dengan mengalirkannya menggunakan pompa DC dan diditeksi menggunakan water flow sensor dengan penutup aliran menggunakan selonoid valve untuk memastikan volume air yang dialirkan telah sesuai dengan hasil sistem pengatur. Input sistem ini terdiri dari nilai EC air penambah (air dengan EC rendah dan air dengan EC tinggi), target EC air pada penampungan yang diinputkan dengan keypad 4x4, nilai EC air pada penampungan yang diditeksi sensor Konduktivitas, dan volume air pada penampungan yang diditeksi dengan menggunakan sensor ultrasonik. Hasil pengujian menunjukkan sistem telah dapat berjalan dengan baik, dimana sistem dapat menditeksi volume air dengan menggunakan sensor ultrasonik dengan error rata-rata penditeksian jarak sebesar 1,92%, penditeksi EC dengan menggunakan sensor Konduktivitas telah dapat menditeksi EC dengan error rata-rata sebesar 1,91%, sistem pengatur volume penambah telah berfungsi dengan baik dengan error rata-rata sebesar 1,87% dan kendali logika fuzzy telah dapat mengambil keputusan banyaknya volume penambah yang harus diberikan untuk mencapai EC target pada penampungan dengan error rata-rata terhadap pengukuran EC setelah hasil pencampuran sebesar 4,17%.
Segmentasi Pelanggan Kartu Halo Telkomsel Berbasis K-Means di Wilayah Sumbagsel: K-Means-Based Customer Segmentation of Telkomsel Kartu Halo in the Sumbagsel Muhammad Bayu Samudra; Alifa Putri Shahabiyah; Aliya Faiza; Edo Wicaksono; Ken Ditha Tania; Allsella Meiriza; Ahmad Rifai
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2607

Abstract

Perusahaan telekomunikasi dalam menjalankan operasionalnya harus mengelola banyak data pelanggan, karena penggunaan layanan digital terus bertambah. Namun, data tersebut biasanya hanya digunakan untuk urusan administratif, sehingga kemampuan untuk menganalisis data guna memahami perilaku pelanggan belum sepenuhnya dimanfaatkan. Penelitian ini secara khusus bertujuan untuk mengelompokkan pelanggan berdasarkan pola penggunaan layanan guna mengidentifikasi karakteristik setiap segmen secara lebih sistematis. Data yang digunakan merupakan data pelanggan pascabayar Kartu Halo wilayah Sumbagsel tahun 2025 yang diperoleh dari PT Telkomsel Smart Office Palembang dengan total 4.645 data. Metode yang digunakan adalah Knowledge Discovery in Databases (KDD), yang terdiri dari beberapa tahap yaitu preprocessing, transformation, dan data mining. Proses pengelompokan dilakukan dengan menggunakan algoritma K-Means, bantuan perangkat lunak RapidMiner. Penelitian menunjukkan bahwa pelanggan bisa dibagi menjadi tiga kelompok dengan kebiasaan menggunakan layanan yang berbeda. Evaluasi menggunakan Davies-Bouldin Index menunjukkan bahwa model clustering memiliki kualitas yang baik dalam memisahkan antarkelompok. Secara ilmiah, penelitian ini membantu dalam penerapan metode clustering untuk membagi pelanggan dalam industri telekomunikasi. Secara nyata, hasil ini bisa dipakai untuk membantu membuat strategi pemasaran yang lebih tepat dan didasarkan pada data.
Co-Authors A. Salwa Aurelya Putri Abd. Rasyid Syamsuri Adelia Rizki Putri Ahmad Fadhil Rizqi Al Amin Mulya Al Farissi Ali Ibrahim Alifa Putri Shahabiyah Aliya Faiza Allsela Meiriza, Allsela Allsella Meiriza Alsella Meiriza Alsella Meiriza Ardina Ariani Athiyyah Nuha Rotifa Aulia Pinkasari Bagus Prihantoro Bambang Tutuko Danny Matthew Saputra Dedy Kurniawan Dhio Pratama Wiransyah Dinda Lestarini Dinna Yunika Hardiyanti Donny Giovanna Karo Karo Dzidan Aditya Gumilang Edo Wicaksono Eka Prasetyo Ariefin Endang Lestari Ruskan Endang Lestari Ruskan Fathoni - Fidela Tertia Alfino Fransiska Prihatini Sihotang Gabriel Sebastian Santoso Gibral Abdurahman Haniifah Putriani Hardini Novianti Hardini Novianti Hardini Novianti Hardini Novianti Huda Ubaya Jaidan Jauhari Jeremiah Alwin Siahaan Kemahyanto Exaudi Ken Dhita Tania Ken Dhita Tania Ken Ditha Tania Kesuma, Lucky Indra Lailla Syal Syabilla Lina Oktarina M Raykah Alam Ramadan M. Rudi Sanjaya M. Thoriqul Fadli Mei Intan Natasyah Meiyin Monica Amilia Putri Melisa Tri Cahya Ningsih Mira Afrina Muhammad Bayu Samudra Muhammad Dzaky Hasyim Muhammad Fachri Nuriza Muhammad Iqbal Disriansyah Muhammad Mayda Ary Pratama Muhammad Naufal Rachmamtullah Muhammad Rafly Muhammad Rendi Muhammad Wahyu Hikmalsyah Octa Dama Yanti Osvari Arsalan Pacu Putra Pascal Adhi Kurnia Tarigan Pibriana, Desi Purwita Sari Puti Chalisa Wardhana Putri Eka Sevtiyuni Putri Rahel Alifia Rahmad Fadli Isnanto Rahmat Izwan Heroza Rahmat Izwan Heroza Rayya Ramadhan Simangunsong Richa Pratiwi Rizka Dhini Kurnia Rossi Passarella Samsuryadi - Sarifah Putri Raflesia Sarmayanta Sembiring Satria Ramadhani Shafa Aurelliza Arian Sutarno - Sutarno Sutarno Syifa Alfariani Syifa Naura Milla Celesta Winda Kurnia