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Journal of Information Technology (JINTECH)
Published by Universitas Islam Negeri Ar-Raniry Banda Aceh
ISSN : 2746234X     EISSN : 27462331     DOI : https://doi.org/10.22373/jintech
Core Subject : Science, Education,
Computer Science & IT Other
This journal provides opportunities for students, lecturers and information technology practitioners to contribute in providing new understanding and concepts related to the basic concepts of computer science that aim to develop information technology. Scope article includes: Information Technology and Islam Software Engineering Human and Computer Interaction Mobile Computing Soft Computing E-Learning Multimedia and Image Processing Parallel/Distributed Computing Artificial Intelligance/Machine Learning Computational Lingustics Data Comunication and Networking Database Management System Big Data and Data Mining
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 53 Documents
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ANALISIS CLUSTERING DAERAH PRODUKTIVITAS PADI DI KABUPATEN DELI SERDANG MENGGUNAKAN ALGORITMA ISOLATION FOREST Ardi Wirya Indarto; Asrianda; Sujacka Retno
Journal of Information Technology (JINTECH) Vol. 7 No. 1 (2026): Februari 2026
Publisher : Prodi Teknologi Informasi UIN Ar-Raniry Bekerjasama dengan Pusat Penelitian dan Penerbitan LP2M Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/jintech.v7i1.9617

Abstract

Rice is a major food commodity that plays a vital role in national food security. However, differences in rice productivity levels between regions pose a challenge in formulating targeted agricultural policies. This study aims to analyze and cluster rice productivity areas in Deli Serdang Regency using the Isolation Forest algorithm. The data used are rice productivity data from all sub-districts in Deli Serdang Regency for the period 2020–2024, with variables of planted area, harvested area, production volume, and rice productivity. The analysis process is carried out through a web-based system using the Python programming language with the Streamlit framework. The Isolation Forest algorithm is used for clustering and anomaly detection, while cluster quality is evaluated using the Silhouette Score. The results of the 2024 data analysis show that 22 sub-districts in Deli Serdang Regency are divided into four clusters: a high-productivity cluster of 7 sub-districts (31.82%) with an average productivity above 6.2 tons/ha, a medium-productivity cluster of 4 sub-districts (18.18%) with a productivity of 6.0–6.1 tons/ha, a low-productivity cluster of 7 sub-districts (31.82%) with a productivity of around 5.9–6.0 tons/ha, and an anomalous cluster of 4 sub-districts (18.18%). The results of this clustering are expected to assist local governments in determining policies to increase rice productivity more effectively and based on data.
PEMANFAATAN TEXT MINING PADA SISTEM REKOMENDASI PEMILIHAN ALGORITMA ENKRIPSI TERBAIK UNTUK KEAMANAN DATA MENGGUNAKAN CONTENT BASED FILTERING M. Iqbal
Journal of Information Technology (JINTECH) Vol. 7 No. 1 (2026): Februari 2026
Publisher : Prodi Teknologi Informasi UIN Ar-Raniry Bekerjasama dengan Pusat Penelitian dan Penerbitan LP2M Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/jintech.v7i1.9660

Abstract

Pemilihan algoritma enkripsi yang tepat merupakan tantangan krusial dalam menjaga keamanan data, namun banyak pengguna menghadapi kesulitan karena keterbatasan pengetahuan teknis dan banyaknya literatur yang harus dianalisis. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi berbasis Content- Based Filtering (CBF) yang terintegrasi dengan teknik text mining untuk membantu proses pemilihan algoritma enkripsi secara lebih cepat, tepat, dan berbasis data ilmiah. Metodologi penelitian yang dilakukan meliputi pengumpulan 300 artikel ilmiah dari portal Garuda Kemdikbud melalui teknik web scraping, yang kemudian melalui tahapan preprocessing seperti tokenisasi, stopword removal, dan case folding. Data tersebut direpresentasikan menggunakan metode TF-IDF, sementara tingkat kemiripan antara kebutuhan pengguna dan literatur dihitung menggunakan Cosine Similarity. Hasil penelitian menunjukkan distribusi algoritma yang paling dominan dibahas dalam literatur adalah RSA (52 artikel), AES (40 artikel), dan RC4 (25 artikel). Sistem rekomendasi yang dibangun terbukti efektif karena mampu memberikan saran yang relevan sesuai kebutuhan spesifik, seperti merekomendasikan AES sebagai algoritma utama untuk kriteria “algoritma cepat untuk data sensitif”. Pemanfaatan text mining dan CBF dalam sistem ini memberikan solusi yang lebih efektif dibandingkan metode manual karena sistem mampu melakukan analisis literatur secara mendalam dan otomatis, sehingga meminimalkan risiko kesalahan pemilihan yang dapat berdampak pada inefisiensi komputasi dan kerentanan keamanan data.
A SYSTEMATIC LITERATURE REVIEW OF NATURAL LANGUAGE PROCESSING FOR INDONESIAN REGIONAL LANGUAGES Ahmadian, Hendri
Journal of Information Technology (JINTECH) Vol. 7 No. 1 (2026): Februari 2026
Publisher : Prodi Teknologi Informasi UIN Ar-Raniry Bekerjasama dengan Pusat Penelitian dan Penerbitan LP2M Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/jintech.v7i1.9797

Abstract

This systematic literature review (SLR) investigates the evolution of Natural Language Processing (NLP) for Indonesian regional languages from 2020 to 2025. Analyzing 13 pivotal studies, the research identifies a significant transition from fragmented studies of high-population languages, such as Sundanese and Madurese, toward inclusive, archipelago-wide frameworks covering low-resource dialects like Acehnese and Nias. Architecturally, the field has progressed from classical machine learning to Transformer-based Large Language Models (LLMs), including IndoBART and GPT. Furthermore, data provenance has evolved from unstructured social media corpora to standardized multilingual benchmarks like NusaX and NusaCrowd. Despite these advancements, persistent gaps in data standardization and large-scale pretraining resources remain. Future research should prioritize cross-lingual transfer learning and specialized benchmarks to ensure the technological sustainability of Indonesia’s diverse linguistic heritage

Page 6 of 6 | Total Record : 53

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