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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis dCartesian: Jurnal Matematika dan Aplikasi Jurnal Buana Informatika JSM (Jurnal SIFO Mikroskil) JUTI: Jurnal Ilmiah Teknologi Informasi JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi Journal of Information System Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi Sistem Informasi dan Aplikasi Indonesian Journal of Computing and Modeling J-SAKTI (Jurnal Sains Komputer dan Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Journal of Information Systems and Informatics Tematik : Jurnal Teknologi Informasi Komunikasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Information Technology Ampera JINAV: Journal of Information and Visualization Journal of Computer and Information Systems Ampera Journal of Information Technology (JIfoTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Locus Penelitian dan Pengabdian JEECS (Journal of Electrical Engineering and Computer Sciences) d'Cartesian: Jurnal Matematika dan Aplikasi Jurnal Abdimas JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Charitas Fibriani
Universitas Kristen Satya Wacana
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mathematics Social Sciences

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ANALISIS KAIDAH ASOSIASI ANTAR ITEM DALAM TRANSAKSI PEMBELIAN MENGGUNAKAN DATA MINING DENGAN ALGORITMA APRIORI (STUDI KASUS: MINIMARKET GUN BANDUNGAN, JAWA TENGAH) Adyawangkara Katon Prasidya; Charitas Fibriani
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 15, No. 2, Juli 2017
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v15i2.a629

Abstract

Data-data transaksi pembelian di minimarket yang selama ini hanya disimpan sebagai arsip dapat dimanfaatkan untuk menjawab masalah pengadaan stok barang, penentuan strategi promosi, dan penataan barang. Solusi pemecahan masalah-masalah tersebut dapat diperoleh menggunakan algoritma apriori, yang dapat digunakan untuk membantu menemukan kaidah asosiasi dalam pembelian item di minimarket. Informasi mengenai kaidah asosiasi dalam transaksi pembelian konsumen dapat dimanfaatkan untuk melakukan pengadaan stok barang yang lebih tepat guna dengan melakukan pengadaan stok barang yang berimbang pada item-item yang sering dibeli secara bersamaan, membuat strategi promosi yang lebih potensial untuk mendongkrak penjualan dengan mengacu pada kombinasi item yang sering dibeli secara bersamaan, dan menata barang di minimarket dengan berorientasi pada item-item yang sering dibeli secara bersamaan. Penelitian ini bertujuan menemukan kaidah asosiasi dalam pembelian item-item di minimarket untuk memecahkan masalah pengadaan stok barang, penentuan strategi promosi, dan penataan barang di minimarket.
Analisis Ulasan Daring Menggunakan Metode Density-Based Spatial Clustering of Applications with Noise Hartono, Edwin; Fibriani, Charitas
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.12363

Abstract

This study applies a density-based clustering method to analyze user perceptions based on reviews on Google Maps. The focus of this research lies in processing dynamic, unlabeled review data to address managers' needs in understanding public sentiment. A total of 399 data sets were collected through Apify, then the data were processed through cleaning, normalization, and stemming stages. Text representation was performed by weighting word frequencies across documents, while WordCloud visualization was utilized to identify dominant words reflecting positive perceptions to help understand the context before the clustering process. The Density-Based Spatial Clustering of Applications with Noise method was applied to form review clusters. The analysis results show that this method is able to group reviews into clusters based on content similarity, although some data were identified as noise. These findings provide useful insights in understanding public perception, thus aiding in strategic decision-making. With the right parameter selection, this method can be an effective approach for further public review sentiment analysis.
Novel Genre Classification based on Synopsis using the Random Forest Algorithm Mahanani, Prananing; Fibriani (SCOPUS ID=57192643331), Charitas
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5815

Abstract

Novel genre classification based on synopses presents a significant challenge in text processing, as each genre exhibits distinct lexical characteristics. This study evaluates the performance of the Random Forest algorithm in classifying novel genres under conditions of imbalanced data distribution. The research stages include text preprocessing—comprising case folding, tokenization, stopword removal, and stemming—feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and model training with Random Forest. In addition, manual data balancing was applied by increasing samples in minority classes through simple oversampling. The model was evaluated using accuracy metrics and confusion matrix analysis. The results indicate that Random Forest is able to identify most genres with moderate accuracy, particularly for classes with larger data volumes. The initial model achieved an accuracy of 42.11%, which increased to 46.67% after the application of data balancing. Misclassification primarily occurred in genres with limited samples that share similar vocabulary with dominant genres. These findings demonstrate that Random Forest can still be applied to synopsis-based novel genre classification without fully relying on balancing techniques. However, performance remains uneven across classes, highlighting the need for per-genre analysis to obtain a more comprehensive evaluation.
Evaluasi Usability Aplikasi SIASAT Mobile Menggunakan System Usability Scale Eldo, Steven; Fibriani, Charitas
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3420

Abstract

The use of mobile academic applications requires good usability to ensure effective services for students. However, the usability level of the Siasat Mobile application at Satya Wacana Christian University has not been systematically evaluated. This study aims to evaluate the usability of the Siasat Mobile application based on user perceptions. The System Usability Scale (SUS) method was employed by involving 100 active students as respondents. Data were collected through a SUS questionnaire and analyzed descriptively to obtain the overall SUS score as well as usability and learnability components. The results show that the application achieved an average SUS score of 62.60, with usability and learnability scores of 62.75 and 62.00, respectively. Based on SUS interpretation models, these scores fall into the OK category, grade C, and Marginal acceptability. These findings indicate that Siasat Mobile is sufficiently usable but still requires improvements in interface consistency, navigation, and ease of learning.Keywords: Usability; System Usability Scale; Siasat Mobile AbstrakPemanfaatan aplikasi mobile akademik menuntut tingkat usability yang baik agar layanan dapat digunakan secara efektif oleh mahasiswa. Namun, hingga saat ini tingkat usability aplikasi Siasat Mobile di Universitas Kristen Satya Wacana belum dievaluasi secara terukur. Penelitian ini bertujuan untuk mengevaluasi usability aplikasi Siasat Mobile berdasarkan persepsi pengguna. Metode yang digunakan adalah System Usability Scale (SUS) dengan melibatkan 100 mahasiswa aktif sebagai responden. Data diperoleh melalui kuesioner SUS dan dianalisis secara deskriptif untuk menghasilkan skor SUS keseluruhan serta komponen usability dan learnability. Hasil penelitian menunjukkan bahwa aplikasi memperoleh skor rata-rata SUS sebesar 62,60, dengan nilai usability 62,75 dan learnability 62,00. Berdasarkan model interpretasi SUS, nilai tersebut berada pada kategori OK, grade C, dan tingkat penerimaan Marginal. Temuan ini menunjukkan bahwa Siasat Mobile cukup dapat digunakan, namun masih memerlukan perbaikan pada konsistensi antarmuka, navigasi, dan kemudahan dipelajari. 
Analisis Usability eOffice UKSW Menggunakan Technology Acceptance Model Mundung, Alfero Timothy; Fibriani, Charitas
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3259

Abstract

Model Klasifikasi Mental Siswa Menggunakan Algoritma Support Vector Machine Charitas Fibriani; Dian Novita Kristiyani
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2813

Abstract

Student mental health plays a vital role in academic performance and social well-being. This study aims to build a classification model using the Support Vector Machine (SVM) algorithm, based on 15 features covering demographic, academic, and behavioral aspects. The dataset, obtained from Kaggle, contains 426 records of junior and senior high school students. Key preprocessing steps include one-hot encoding, feature standardization, train-test splitting (80:20), and handling class imbalance with SMOTE. The model was trained using the Radial Basis Function (RBF) kernel and optimized using Grid Search CV to find the best parameters. Evaluation results show 65% accuracy, with better performance in predicting students without mental health issues (Absence). However, low recall for the Presence class indicates a need for improved strategies to handle data imbalance. This study highlights the potential of machine learning, particularly SVM, as a tool for early mental health detection in students, provided that effective data preprocessing is applied.Keywords: Student mental health; classification; Support Vector Machine; SMOTE; machine learningAbstrakKesehatan mental siswa berpengaruh besar terhadap prestasi akademik dan kesejahteraan sosial. Penelitian ini bertujuan membangun model klasifikasi kondisi mental siswa menggunakan algoritma Support Vector Machine (SVM) berbasis 15 fitur demografis, akademik, dan perilaku. Dataset yang digunakan berasal dari Kaggle, terdiri atas 426 data siswa SMP dan SMA. Tahapan penelitian meliputi preprocessing dengan one-hot encoding, standarisasi numerik, pembagian data (80:20), serta penanganan ketidakseimbangan data menggunakan SMOTE. Model dilatih menggunakan kernel Radial Basis Function (RBF) dan dioptimasi dengan Grid Search CV. Hasil evaluasi menunjukkan akurasi sebesar 65%, dengan kinerja lebih baik dalam mengenali siswa tanpa gangguan mental (absence) dibandingkan siswa dengan gangguan mental (presence). Rendahnya recall pada kelas Presence mengindikasikan perlunya strategi lanjutan terhadap ketidakseimbangan data. Penelitian ini menunjukkan bahwa machine learning, khususnya SVM, berpotensi sebagai alat bantu dalam deteksi awal kesehatan mental siswa jika disertai pengolahan data yang tepat.Kata kunci: Kesehatan mental siswa; Klasifikasi; Support Vector Machine; SMOTE; Machine learning 
Co-Authors Adenia Kusuma Dayanthi Adyawangkara Katon Prasidya Alamsyah, Samuel Sandy Andeka Rocky Tanaamah Andhika Dwi Putra Bagaskara Suprapto Suprapto Andrian, Joshua Valens Antar Maramba Jawa Antonius Mbay Ndapamury Ardian Ariadi Arya, William Cadwell Marthin Rumagit Chinta, Caroline Amanda Desy Angeline Dian Novita Kristiyani Diane Junianti Dody Agung Saputro Eldo, Steven Elvira Umar Endrick Fernando Suhono Gandhi Alip Wijayanto Gloria Milenda Jesika Prima Gudiato, Candra Halim Yosephine Hanita Yulia HARTONO, DAVID Hartono, Edwin Hindriyanto Dwi Purnomo Hiskia, Cheryl Louisa Loedwyca Irawati, Susi Kurniawan, Restu Yoga Loppies, Marthin Wilheim Lumban Batu, Juliana Andretha Janet Mahanani, Prananing Merryana Lestari Mira Mira Mundung, Alfero Timothy Octavianus Dicky Wahyudi Panggabean, Albert Jonathan Pratama, Nathan Putra Prima, Genaro Asa Puntoriza Puntoriza Putra, Marcellino Adi Ramba, Rileanly Masakke Ramos Somya Ravensca Matatula Raymond Elias Mauboy Sambadagni, Sabella Yasmin Silvia Yolanda Sastanti Solly Aryza Somya Ramos Sri Yulianto Joko Prasetyo Stanny Dewanty Rehatta Stevanus Dwi Istiavan Mau Suryana, Steven Immanuel Sutrisno, Ezra Harfi T. Arie Setiawan Prasida Triloka Mahesti Valentino Kevin Sitanayah Que Vinsensius Aprila Kore Dima Virginia, Mila Kavita Wata, Denis Christo Van Halen William Christanto Winsy Weku Winsy Weku Yerik Afrianto Singgalen Yerymia Alfa Susetyo Yohannes Neman, Pius