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All Journal International Journal of Electrical and Computer Engineering Jupiter Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal on Computing (Indo-JC) JOIV : International Journal on Informatics Visualization Al Ishlah Jurnal Pendidikan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) Jurnal Teknologi Terpadu STRING (Satuan Tulisan Riset dan Inovasi Teknologi) MULTINETICS Jurnal Mantik JATI (Jurnal Mahasiswa Teknik Informatika) Sains, Aplikasi, Komputasi dan Teknologi Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Charity : Jurnal Pengabdian Masyarakat Journal of Software Engineering, Information and Communication Technology Joutica : Journal of Informatic Unisla Jurnal Teknik Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Asian Journal of Community Services (AJCS) eProceedings of Engineering Tekmulogi: Jurnal Pengabdian Masyarakat Journal of Law, Education and Business Scientica: Jurnal Ilmiah Sains dan Teknologi Journal of Information Technology, Software Engineering and Computer Science Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Journal of Mathematics, Computation and Statistics (JMATHCOS)
Ichwanul Muslim Karo Karo
Universitas Negeri Medan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Indonesian Elementary Students’ Perceptions of Teachers’ Affective Support: A Cluster Analysis Using National Literacy and Numeracy Assessment Data Rizki Habibi; Muliawan Firdaus; Ichwanul Muslim Karo Karo
AL-ISHLAH: Jurnal Pendidikan Vol 17, No 4 (2025): DECEMBER 2025
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v17i4.7754

Abstract

Affective support from teachers—such as academic expectations, attention and care, and constructive feedback—plays a critical role in students’ learning outcomes but is often underexplored in large-scale educational assessments, particularly in developing countries. This study examines how Indonesian elementary students perceive teacher affective support and how these perceptions relate to their literacy and numeracy performance. Using data from the 2023 Indonesian National Assessment involving 214,481 fifth-grade students, we employed K-Means clustering to identify latent student profiles based on their literacy, numeracy, and self-reported perceptions of teacher support. Variables were normalized, and the optimal number of clusters was determined using the Elbow, Silhouette, and Davies-Bouldin methods. Five distinct student clusters emerged, each characterized by unique combinations of academic achievement and affective perceptions. High-achieving students consistently reported more positive perceptions of teacher support, particularly in terms of feedback and expectations. ANOVA tests confirmed significant differences (p 0.001) across clusters in all affective and academic variables, with moderate to large effect sizes. The findings highlight the alignment between academic success and perceived teacher affective support. This clustering approach reveals nuanced student profiles that traditional methods may overlook, offering a data-driven foundation for differentiated teaching, teacher training, and policy interventions. Clustering national assessment data provides actionable insights for enhancing affective support in classrooms. The methodology is scalable and adaptable for use in other educational systems seeking to personalize instruction and promote equity.
An optimized kernel SVM framework for game review sentiment analysis using particle swarm optimization Karo Karo, Ichwanul Muslim; Dewi, Sri; Nasution, Alvi Sahrin
Joutica Vol 11 No 1 (2026): MARET
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jti.v11i1.1633

Abstract

The digital game industry’s rapid growth has increased interaction between developers and players through online review platforms. These reviews contain vital information about gaming experiences and satisfaction, serving as guides for future game versioning. Sentiment analysis provides a strategic approach to automatically classify reviews, offering data-driven insights for developers. This study focuses on enhancing sentiment analysis performance for Player Unknown's Battlegrounds (PUBG) reviews by integrating Kernel Support Vector Machine (SVM) with Particle Swarm Optimization (PSO). A dataset of 1,205 reviews from the Google Play Store was analyzed using TF-IDF feature extraction and 5-fold cross-validation. While default Kernel SVM achieved 76.78% accuracy, it suffered from low precision (56.9%). Implementing PSO for parameter optimization significantly improved performance, reaching 86.42% accuracy, 70.98% precision, and an 83.03% F1-score. Comparisons with Naïve Bayes, basic SVM, BERT, and Lexicon + SVM confirm that the Kernel SVM + PSO model provides superior and more stable performance. These findings highlight PSO’s effectiveness in SVM parameter tuning. Future research should investigate combining metaheuristic optimization with deep learning models to improve model generalization.
Ekstraksi Informasi Bencana Banjir Dari Berita Online Berbasis Named Enitity Recognition Ichwanul Muslim Karo Karo; Sri Dewi; Alvin Syahrin
MULTINETICS Vol. 11 No. 1 (2025): MULTINETICS Mei (2025)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v11i1.7499

Abstract

Banjir merupakan bencana yang paling sering terjadi di Indonesia, sehingga informasi cepat dan akurat tentang lokasi terdampak menjadi sangat penting. Penelitian ini bertujuan untuk mengembangkan model Named Entity Recognition (NER) berbasis Conditional Random Field (CRF) guna mengekstrak entitas penting (nama kota dan waktu) dari berita online terkait banjir. Data diambil dari 200 artikel berita tahun 2023 melalui metode web scraping dan manual dari lima portal berita nasional. Proses preprocessing mencakup pembersihan teks, tokenisasi, stemming, stopword removal, dan POS tagging. Data kemudian dilabeli menggunakan metode chunking dan IOB tagging untuk meningkatkan performansi identifikasi entitas. Evaluasi menunjukkan bahwa penggunaan IOB tagging pada CRF meningkatkan kinerja model, dengan F1-score = 93.6%. Hasil penelitian ini menunjukkan bahwa CRF efektif dalam mengekstrak informasi bencana banjir, dan dapat mendukung pengambilan keputusan cepat dalam upaya penanggulangan bencana.
An optimized kernel SVM framework for game review sentiment analysis using particle swarm optimization Ichwanul Muslim Karo Karo; Sri Dewi; Alvi Sahrin Nasution
Joutica Vol 11 No 1 (2026): MARET
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jti.v11i1.1633

Abstract

The digital game industry’s rapid growth has increased interaction between developers and players through online review platforms. These reviews contain vital information about gaming experiences and satisfaction, serving as guides for future game versioning. Sentiment analysis provides a strategic approach to automatically classify reviews, offering data-driven insights for developers. This study focuses on enhancing sentiment analysis performance for Player Unknown's Battlegrounds (PUBG) reviews by integrating Kernel Support Vector Machine (SVM) with Particle Swarm Optimization (PSO). A dataset of 1,205 reviews from the Google Play Store was analyzed using TF-IDF feature extraction and 5-fold cross-validation. While default Kernel SVM achieved 76.78% accuracy, it suffered from low precision (56.9%). Implementing PSO for parameter optimization significantly improved performance, reaching 86.42% accuracy, 70.98% precision, and an 83.03% F1-score. Comparisons with Naïve Bayes, basic SVM, BERT, and Lexicon + SVM confirm that the Kernel SVM + PSO model provides superior and more stable performance. These findings highlight PSO’s effectiveness in SVM parameter tuning. Future research should investigate combining metaheuristic optimization with deep learning models to improve model generalization.
Optimasi Klasifikasi Pola Detak Jantung Menggunakan Particle Swarm Optimization (PSO) dan Algoritma XGBoost Ichwanul Muslim Karo Karo; Justaman Arifin Karo Karo; Manan Ginting; Darni Paranita; Ratna Kristina Tarigan; Maulidna Maulidna
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 4 No. 4 (2026): Volume 4 Number 4 October 2026 (Issue in Progress)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v4i4.372

Abstract

Pemantauan detak jantung merupakan salah satu pendekatan penting dalam mendukung deteksi dini gangguan kardiovaskular. Perkembangan Internet of Things (IoT) memungkinkan proses akuisisi data fisiologis dilakukan secara real-time melalui perangkat wearable, namun pemanfaatan data tersebut masih menghadapi tantangan dalam menghasilkan model klasifikasi yang akurat. Penelitian ini bertujuan mengoptimalkan proses klasifikasi pola detak jantung normal dan abnormal menggunakan algoritma Extreme Gradient Boosting (XGBoost) yang dipadukan dengan Particle Swarm Optimization (PSO) sebagai metode hyperparameter tuning. Dataset penelitian diperoleh dari hasil pengukuran detak jantung mahasiswa Program Studi Ilmu Komputer Universitas Negeri Medan menggunakan sensor MAX30102 yang terintegrasi pada perangkat IoT berbasis ESP32-C3 Mini. Tahapan penelitian meliputi akuisisi data, preprocessing, ekstraksi fitur statistik, pembangunan model baseline XGBoost, optimasi threshold klasifikasi menggunakan PSO, serta evaluasi performa model berdasarkan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa optimasi PSO mampu meningkatkan performa model dari akurasi 90% (baseline) menjadi 100%, dengan threshold optimal pada rentang 68,44–97,82 BPM yang memberikan proses inferensi sederhana dan cepat. Temuan ini menunjukkan bahwa integrasi PSO dengan XGBoost efektif dan efisien untuk diterapkan pada sistem klasifikasi detak jantung berbasis IoT secara real-time
Co-Authors Abil Mansyur, Abil Adawiah Hasyani, Rabiahtul Ade Amelia, Tasya Adidtya Perdana, Adidtya Aditia Sanjaya Ahyar, Khoirul Alvi Sahrin Nasution Alvin Syahrin Ananda Khosuri Angelina Prima Kurniati Anggraini, Nisa Putri Aqila Aqila, Aqila Azizul Azhar Ramli Bachruddin Saleh Luturlean Bakti Dwi Waluyo Darari, Muhammad Badzlan Darni Paranita Daulay, Leni Karmila Dedy Kiswanto Dian Septiana Dimas Pebrian Supandi Ester Berliana Ritonga, Yolanda Evelyn Keisha Silalahi Eviyona Laurenta Br Barus Fadillah, Wahyu Nur Falah, Miftahul Fitri Rahayu Fitria, Nur Anisa Gea, Kurnia Mildawati Ginting, Manan Gunawan, Rizky Habibi, Rizki Haraha, Melyana Hariyanto HARIYANTO HARIYANTO Hariyanto Hariyanto Hariyanto, Hariyanto Hendriyana Hendriyana Heru Nugroho Husna Batubara, Shabrina Ida Ayu Putu Sri Widnyani Jodi Kusuma Juan Steiven Imanuel Septory Justaman Arifin Karo Karo Justaman Arifin Karo Karo Karo karo, Justaman Arifin Karo Karo, Justaman Arifin Landong, Ahmad Lorinez S, Yohana Manan Ginting Manan Ginting Mardiana Mardiana Maretha Br. Simbolon, Silvana Maulana Malik Fajri Maulidna, Maulidna Melania Justice Panggabean Miftahul Falah Miftahul Falah Mohd Farhan Md Fudzee Mohd Farhan MD Fudzee, Mohd Farhan Molliq Rangkuti, Yulita Mufida, Yasmin Muhammad Yusuf Muliawan Firdaus Mutiara Sihaloho, Laura Adelia Nasution, Alvi Sahrin Nasution, Aurela Khoiri Natasya, Amanda Nelza, Novia Nur Hafni Nurul Ain Farhana Nurul Ikhsan Panggabean, Suvriadi Permata Putri Pasaribu, Yohanna Purba, Desni Paramitha Putri Harliana Putri Maulidina Fadilah Ramadhani, Fanny Ramanti Dharayani Ramli, Azizul Azhar Rangkuti, Y. M Ratna Kristina Tarigan Reinaldo Kenneth Darmawan Rennyta Yusiana Retno Setyorini Roby Dwi Hartanto Rohmat Saragih Romia Romia Said . Iskandar Salsabila, Aqila Shahreen Kasim Shahreen Kasim, Shahreen Simamora, Elmanani Sisti Nadia Amalia Sri Dewi Sri Dewi Sri Dewi Sri Suryani Supra Yogi Valentino, Bob Wahyu Nur Fadillah Wardhani Muhamad Warjaya, Angga Wibowo, Adinda Widi Astuti winsyahputra Ritonga Yahya Peranginangin Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yunianto Yunianto Yunianto Yunianto Yunianto Yunianto, Yunianto ZK Abdurahman Baizal