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All Journal Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Ilmu Komputer dan Teknik Informatika
Hafiz Aryanda
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Computer Science & IT Engineering

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Analisis Usability Aplikasi Perpustakaan Nasional Melalui Pendekatan Heuristic Evaluation Berbasis Human Computer Interaction Randy, Muhammad Randy Fachrezi; Hafiz Aryanda; Muhammad Farhan; Dimas Aqila Aptanta; Arief Denis Walidein
Jurnal IT UHB Vol 6 No 2 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i2.1851

Abstract

Rapid advancements in technology have driven the emergence of various digital applications, including digital libraries. One such application is iPusnas, which serves as a digital literacy platform among users. Based on user reviews of the Play Store, many users have reported issues such as the app’s slow responsiveness and non-functional filters, such as the inability to sort alphabetically. These problems indicate a potential deficiency in the usability of an application. Therefore, a usability evaluation is needed to determine the extent to which iPusnas adhere to the principles of Human-Computer Interaction (HCI). The method employed is Jakob Nielsen’s Heuristic Evaluation, which assesses ten usability principles, including consistency, error prevention, and minimalist design. The evaluation was conducted by five internal evaluators, using a severity rating scale ranging from 0 to 4. The results show that iPusnas continue to experience usability issues in several areas, particularly in the principles of Help and Documentation (4.00), Flexibility and Efficiency of Use (3.33), and Visibility of System Status (3.13). The principle of Error Prevention received the lowest severity score (1.20), suggesting that it functions relatively well. This study underscores the effectiveness of multi-evaluator heuristic evaluation in identifying application issues, and serves as a foundation for future improvements aimed at enhancing user satisfaction.
Prototype Penggabungan Fuzzy Dan Naive Bayes dalam Analisis Citra Dokumen Untuk Penilaian Margin Laporan Praktikum UINSU Hafiz Aryanda; Randy, Muhammad Randy Fachrezi; Arif Dennis Walidein; Dimas Aqila Aptanta; Alwi Syahputra
Jurnal IT UHB Vol 6 No 2 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i2.1854

Abstract

Manual assessment of practicum report margins often faces challenges due to variations in document layout and the large number of reports that must be reviewed, resulting in an inefficient and subjective evaluation process. Therefore, this study aims to develop a prototype that serves as a guideline for automated practicum report assessment. The proposed research introduces a hybrid model integrating Fuzzy Logic and Naive Bayes. Evaluation results show that the model without fuzzy achieved an accuracy of 73.33% but exhibited bias toward the majority class, with low recall for the "Rejected" class. In contrast, the Fuzzy Naive Bayes model improved accuracy to 80% and produced more balanced classification performance, with significant increases in recall and F1 Score for the minority class. The integration of fuzzy logic effectively enhances the detection of margin non-compliance.
Sistem Klasifikasi Diabetes Mellitus Menggunakan Algoritma K-Nearest Neighbor (KNN) Berbasis Web Muhammad Randy Fachrezi; Hafiz Aryanda; Alwi Syahputra; Risma Riansyah
Jurnal Ilmu Komputer dan Teknik Informatika Vol. 2 No. 1 (2026): Januari 2026
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/juikti.v2i1.116

Abstract

Diabetes mellitus merupakan penyakit metabolik kronis dengan prevalensi yang terus meningkat di Indonesia, mencapai 11,3% dari total populasi berdasarkan data International Diabetes Federation tahun 2024. Permasalahan utama dalam penanganan diabetes adalah keterlambatan diagnosis yang menyebabkan komplikasi serius. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi diabetes mellitus berbasis web menggunakan algoritma K-Nearest Neighbor (KNN) guna mendukung deteksi dini secara praktis dan mudah diakses. Dataset yang digunakan adalah Pima Indians Diabetes Database yang terdiri dari 768 data dengan 8 atribut. Tahapan pre-processing data meliputi imputasi median untuk menangani nilai anomali, penanganan outlier menggunakan metode Interquartile Range (IQR), penyeimbangan data dengan teknik Synthetic Minority Over-sampling Technique (SMOTE), serta normalisasi Min-Max. Model KNN diimplementasikan dengan nilai K optimal sebesar 8 yang diperoleh melalui cross-validation. Hasil evaluasi menunjukkan akurasi pada data testing sebesar 73,68% dengan precision 71,15%, recall 78,72%, F1-score 74,75%, dan nilai ROC-AUC 0,8132. Sistem web dirancang menggunakan arsitektur client-server dengan React.js sebagai frontend dan Python Flask sebagai backend. Antarmuka sistem menyediakan fitur edukasi, form input data kesehatan, serta halaman hasil klasifikasi yang informatif. Hasil penelitian menunjukkan bahwa sistem mampu mengklasifikasikan risiko diabetes dengan performa yang cukup baik dan dapat digunakan sebagai alat bantu skrining awal.
Co-Authors Alwi Syahputra Arief Denis Walidein Arif Dennis Walidein Dimas Aqila Aptanta Muhammad Farhan Muhammad Randy Fachrezi Randy, Muhammad Randy Fachrezi Risma Riansyah