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All Journal Ulul Albab: Jurnal Studi Islam MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknologi dan Manajemen Informatika OPSI SMATIKA Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Indonesian Journal of Artificial Intelligence and Data Mining JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Mantik ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Mnemonic G-Tech : Jurnal Teknologi Terapan International Journal of Advances in Data and Information Systems Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) J-Intech (Journal of Information and Technology) Didaktika Religia Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Journal of Information Technology
Ririen Kusumawati
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Religion Aerospace Engineering Arts Humanities 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 Library & Information Science Social Sciences Other

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Enhancing Student Engagement in Islamic Religious Education Through a Finite-State Machine and Guided Inquiry-Based Serious Game Marandy, Yuniar Setyo; Nugroho, Fresy; Kusumawati, Ririen; Handayani, Tuti; Marudin, Marudin
Didaktika Religia Vol. 13 No. 2 (2025): December
Publisher : Postgraduate Program, Institut Agama Islam Negeri (IAIN) Kediri, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30762/didaktika.v13i2.3611

Abstract

This study aims to develop interactive learning media in the form of a serious game based on Finite State Machine (FSM) and guided inquiry approach to improve student engagement and understanding of Islamic Education material, especially the exemplary story of Ashabul Kahfi. The research method used is Research and Development (R&D) with the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) development model. The game was developed with enemy characters based on Finite State Machine (FSM), consisting of three main conditions: patrol, pursuit, and return to the path, to create dynamics and challenges in educational games. Fourth-grade elementary school students were the main subjects in testing the effectiveness of the media, which involved pre-tests and post-tests to measure learning outcome improvement, as well as validation by subject matter and media experts. The results showed that the developed media met the feasibility criteria with a subject matter expert validation score of 3.5 and a media expert validation score of 4.0, as well as an N-Gain value of 0.53, which is in the moderate category. These findings indicate that the combination of FSM and guided inquiry methods in serious games can create an interactive, enjoyable, and effective learning experience that increases student engagement and critical thinking skills in PAI material. The research results recommend this media as an alternative for interactive digital learning at the elementary school level.
Development of Academic Community Recommendation System Using Content-Based Filtering at UIN Malang Informatics Engineering Study Program Abdurrozzaaq Ashshiddiqi Zuhri; Ririen Kusumawati; Muhammad Ainul Yaqin; Aldian Faizzul Anwar; Achmad Fahreza Alif Pahlevi
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1916

Abstract

The mismatch between the number and quality of Information and Communication Technology (ICT) talents and industry needs in Indonesia creates significant challenges, especially for Informatics Engineering students who often experience difficulties in determining the appropriate professional field. This research aims to develop a content-based filtering-based academic community recommendation system to help students choose communities that are relevant to their interests, skills and experience. The system uses TF-IDF and cosine similarity methods to match student profiles with community descriptions. Data was collected from 48 students and 10 academic communities in the Informatics Engineering Study Program of UIN Malang, and processed through preprocessing stages before modeling. Evaluation results using the System Usability Scale (SUS) resulted in a score of 76, which is categorized in the “good” level, However, users indicated the need for improved guidance in navigating the system. This system is expected to be an innovative solution to increase student participation in appropriate academic communities, as well as support the development of their potential and readiness for the world of work
Ashabul Kahfi Serious Game with Adaptive Recommendation System Based on Knowledge-Based Filtering and MULTIMOORA for Islamic Education Yuniar Setyo Marandy; Fresy Nugroho; Ririen Kusumawati
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16082

Abstract

This study aims to develop a serious game based on the story of Ashabul Kahfi using a difficulty level recommendation system based on Knowledge-Based Filtering (KBF) and the MULTIMOORA method. This game is designed for elementary school students in the context of Islamic religious education, instilling moral and spiritual values through the narrative of the story of Ashabul Kahfi. The difficulty level in the game is adjusted to the player's profile based on age, experience, and preferences obtained through a questionnaire. The MULTIMOORA method is applied to process questionnaire data and provide adaptive and personalized difficulty level recommendations. The results of the study show that the application of this recommendation system is able to increase student learning motivation and learning effectiveness by providing challenges that are appropriate to each player's abilities. Thus, this study contributes to the development of adaptive and effective game-based learning media, particularly in improving the understanding of religious values among students.
Evaluasi dan Analisis Domain Shift Model NER pada Industri Game Berbahasa Indonesia Wibowo, Firmansyah Rekso; Abidin, Zainal; Kusumawati, Ririen
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13591

Abstract

Indonesia’s gaming industry is rapidly expanding and produces extensive textual data from diverse sources such as news articles and social media. Named Entity Recognition (NER) models can extract valuable information from this data; however, general-purpose models remain suboptimal for the gaming domain due to its unique terminology. This study evaluates the impact of domain shift on the NERGrit model, a standard NER model from the IndoNLU benchmark, when applied to an Indonesian gaming text corpus. The model was tested on the gaming-domain corpus and compared with a domain-specific lexicon to identify error patterns through qualitative and quantitative analyses. Results show that although NERGrit can detect numerous entities, it often fails to classify them correctly. The dominance of the MISC category (61.8%) and recurring issues such as misclassification, entity boundary errors, and ambiguity between fictional and real entities indicate the model’s limitations. This study confirms the existence of domain adaptation challenges and introduces a new entity schema covering the categories GAME, PLATFORM, TECH, EVENT, CHAR,and COMPANY. The proposed schema provides a foundation for developing a more relevant NER dataset and model tailored to Indonesia’s gaming industry ecosystem.
Educational Data Mining in Online Learning: Data Mining Techniques and Algorithms, Factors, Equity and Accessibility Dimensions (A Systematic Literature Review) Hidayah, Imalatul; Kusumawati, Ririen
G-Tech: Jurnal Teknologi Terapan Vol 10 No 1 (2026): G-Tech, Vol. 10 No. 1 January 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i1.8628

Abstract

This Systematic Literature Review (SLR) examines the application of Educational Data Mining (EDM) in online learning from 2015 to 2025 using the PRISMA approach. Thirty-two studies were analyzed to identify the data mining techniques used, the factors analyzed, and the extent to which the literature considers the equity and accessibility dimensions. The review results indicate that EDM is widely applied to predict academic performance, identify learning behavior patterns, detect at-risk students, and analyze the use of learning resources. The dominant techniques include classification, prediction, sequence analysis, process mining, and clustering. However, the equity and accessibility aspects are rarely discussed explicitly most studies only implicitly address accessibility through digital interaction behavior, while social factors related to equity, such as learning readiness, environmental support, and the digital divide, appear in only a small proportion. Furthermore, the variety of data formats and limited course coverage limit the generalizability of the findings. Overall, this study emphasizes the need for stronger integration between educational analytics and the social dimension for EDM to more effectively support equitable distribution of quality and access to online learning.
Predicting Budget Absorption Categories Using Random Forest and Support Vector Machine Methods Novardy, Novardy; Kusumawati, Ririen; Hariyadi, Muhammad Amin; Harini, Sri; Imamudin, Muhammad
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.37223

Abstract

Budget classification plays a crucial role in planning, management, and budgeting, from implementation to accountability. We create budgets by considering various types of expenditures and funding sources. Each type of expenditure, such as employee salaries, goods, capital, grants, social assistance, subsidies, interest, and non-tax revenue (PNBP) or public service agencies (BLU), has its own set of rules and methods for tracking money. This study aims to demonstrate how budget classification, based on expenditure types and funding sources, is applied in the implementation of the Revenue Budget. This study aims to assess the classification performance of two models, namely the Random Forest Classifier (RFC) and Support Vector Machine (SVM), based on historical data and evaluate the performance of each model. Tests show that the Random Forest model consistently outperforms the SVM model for each data proportion, with a ratio of 90:10 to 60:40. The Random Forest model achieved its best performance at the 80:20 data split, with an accuracy score of 94 percent, a precision score of 94 percent, a recall score of 94 percent, and an F1 score of 87 percent. The average accuracy score of the SVM test results was 80 percent.
KLASIFIKASI BERITA HOAKS BAHASA INDONESIA MENGGUNAKAN INDOBERT FINE-TUNING DENGAN PENDEKA-TAN FOCAL LOSS PADA DATA TIDAK SEIMBANG Kunaefi, Aang; Abidin, Zainal; Kusumawati, Ririen
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.7811

Abstract

Penyebaran berita hoaks di media online menjadi isu serius di tengah meningkatnya konsumsi informasi digital di kalangan masyarakat. Klasifikasi berita hoaks berbahasa Indonesia memiliki peran penting untuk menekan penyebaran informasi palsu. Salah satu tantangan utama dalam sistem klasifikasi ini adalah ketidakseimbangan distribusi data, di mana jumlah berita non-hoaks jauh lebih banyak dibanding-kan berita hoaks. Penelitian ini mengusulkan pendekatan klasifikasi berita hoaks berbahasa Indonesia melalui teknologi Natural Lan-guange Processing (NLP) menggunakan fine-tuning model IndoBERT, yang merupakan pre-trained language model berbasis arsitektur BERT (Bidirectional Encoder Representations from Transformers) dan dis-esuaikan untuk Bahasa Indonesia. Ketidakseimbangan data diatasi menggunakan metode Focal Loss. Pendekatan focal loss dirancang untuk lebih menekankan pembelajaran pada sampel kelas minoritas yang sulit diklasifikasikan. Penelitian ini menggunakan dataset dari platform Kaggle, Huggingfase dan Mendeley. Tataset mencakup berita Bahasa Indonesia dengan jumlah data berita hoaks jauh lebih kecil dari berita faktual. Hasil evaluasi menunjukkan bahwa kombinasi In-doBERT dan Focal Loss mampu meningkatkan performa model dengan akurasi sebesar 98.3% dibandingkan dengan pendekatan Cross-Entropy Loss yang mendapat akurasi 97% Penelitian ini menun-jukkan bahwa penggabungan model berbasis bahasa alami dengan strategi penanganan data tidak seimbang dapat memberikan hasil yang lebih akurat dalam mendeteksi berita hoaks.
Co-Authors A, Miftahul Hikmah Putri Samudera Aang Subiyakto Abd. Rahman Ahlan Abdurrozzaaq Ashshiddiqi Zuhri Achmad Fahreza Alif Pahlevi Agung Teguh Wibowo Almais Agus Sofiyan Anwar, Agus Sofiyan Ahmad Fahmi Karami Ainul Yaqin Aldian Faizzul Anwar Anwar, Aldian Faizzul Arief, Yunifa Miftachul Asrul Sani Azmi, Agus N Balogun, Naeem A Cahyo Crysdian Dita Aisha Dwi Purbo Yuwono Dwi Yuniarto Eko Agus Moh. Iqbal Erfan Ainul Yakin Fachrul Kurniawan Fathurrahman Fathurrahman Fithriani Matondang, Fithriani Fresy Nugroho Fresy Nugroho Hariyadi, Muhammad Amin Hartawan, Muhammad S Hidayah, Ika Arofatul Hidayah Hidayah, Imalatul Huda, Muhammad Q Ida Ayu Putu Sri Widnyani imamudin Imamudin, M Imamudin, Muhammad Irwan Budi Santoso Kunaefi, Aang Kurniawati Kurniawati Lia Wahyuliningtyas MARIA BINTANG Marudin, Marudin Maulidifa, Renisa Mokhamad Amin Hariyadi Muchammad Mustaqhfiri, Muchammad Muhammad Andryan Muhammad Andryan Wahyu Saputra Muhammad Faisal Muhammad Isa Ansori Muji, Muji Nashrul Hakiem Novardy, Novardy Nur Fitriyah Ayu Tunjung Sari Pahlevi, Achmad Fahreza Alif Puspa Miladin Nuraida Safitri A. Silfiyah, Chilmiatus Sri Harini Subarkah, Aan Fuad Sulika Sulika Suryatno, Agung Suseno, Hendra B Syawab, Moh Husnus Totok Chamidy Usman Pagalay Viva Arifin Wahyuliningtyas, Lia Wibowo, Firmansyah Rekso Wiwik Handayani Yuliawan, Audi Bayu Yuniar Setyo Marandy Yunifa Miftachul Arif Yunifa Mittachul Arif Yusril Haza Mahendra Zainal Abidin Zainal Abidin Zuhri, Abdurrozaq Ashshiddiqi Zuhri, Abdurrozzaaq Ashshiddiqi