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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) 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 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 Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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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
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.
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.
Predicting Budget Absorption Categories Using Random Forest and Support Vector Machine Methods Novardy Novardy; Ririen Kusumawati; Muhammad Amin Hariyadi; Sri Harini; Muhammad Imamudin
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.
PENILAIAN KINERJA PEGAWAI DENGAN METODE TOPSIS DAN BACKPROPAGATION NEURAL NETWORK Audi Bayu Yuliawan; M. Amin Hariyadi; Ririen Kusumawati; Cahyo Crysdian; Fresy Nugroho
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.v10i2.7826

Abstract

Transformasi digital melalui penerapan Industri 4.0 dan e-Government telah mengubah paradigma administrasi publik, sehingga menuntut sistem evaluasi kinerja pegawai yang lebih adaptif dan objektif. Penelitian ini bertujuan untuk mengklasifikasikan kinerja pegawai ke dalam lima kategori, yaitu "sangat baik", "baik", "cukup", "buruk", dan "sangat buruk", dengan menggunakan pendekatan Neural Network Backpropagation. Metodologi yang digunakan mencakup beberapa tahapan utama, dimulai dari proses preprocessing data yang menge-lompokkan kriteria penilaian ke dalam empat aspek: kualifikasi, kom-petensi, kinerja, dan disiplin. Selanjutnya, dilakukan seleksi fitur menggunakan metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), dan hasilnya digunakan sebagai data pelatihan pada model Neural Network Backpropagation. Hasil pelati-han menunjukkan performa model yang cukup baik, dengan nilai loss dan Mean Squared Error (MSE) sebesar 0,000465, Mean Absolute Per-centage Error (MAPE) sebesar 19,59%, dan akurasi mencapai 80,41%. Sementara itu, hasil eksperimen dengan metode TOPSIS secara terpisah mencatat akurasi sebesar 81% dan nilai loss sebesar 0,377. Kombinasi metode TOPSIS dan Neural Network Backpropagation ter-bukti efektif dalam mengklasifikasikan kinerja pegawai secara konsis-ten. Temuan ini memberikan kontribusi terhadap pengembangan sis-tem evaluasi kinerja berbasis kecerdasan buatan yang lebih akurat dan adaptif terhadap tantangan administrasi publik modern.
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 Audi Bayu Yuliawan Azmi, Agus N Balogun, Naeem A Cahyo Crysdian 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 Hartawan, Muhammad S Hidayah, Ika Arofatul Hidayah Huda, Muhammad Q Ida Ayu Putu Sri Widnyani imamudin Imamudin, M Irwan Budi Santoso Kunaefi, Aang Kurniawati Kurniawati Lia Wahyuliningtyas M. Amin Hariyadi MARIA BINTANG Marudin, Marudin Maulidifa, Renisa Mokhamad Amin Hariyadi Muchammad Mustaqhfiri, Muchammad Muhammad Amin Hariyadi Muhammad Andryan Muhammad Andryan Wahyu Saputra Muhammad Faisal Muhammad Imamudin 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 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 Yuniar Setyo Marandy Yunifa Miftachul Arif Yunifa Mittachul Arif Yusril Haza Mahendra Zainal Abidin Zainal Abidin Zuhri, Abdurrozaq Ashshiddiqi Zuhri, Abdurrozzaaq Ashshiddiqi