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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 JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran 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) Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Locus Penelitian dan Pengabdian JEECS (Journal of Electrical Engineering and Computer Sciences) d'Cartesian: Jurnal Matematika dan Aplikasi semanTIK Jurnal Abdimas JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal DIMASTIK
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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mathematics Social Sciences

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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. 
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 
Pengelompokan Komoditas Ekspor Laut Indonesia Berbasis K-Means Clustering dalam Sistem Informasi WEB Juseka, Christien Julio; Fibriani, Charitas
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.13737

Abstract

This research presents the design of a web-based information system (SILAUTINA) for clustering Indonesia's marine export commodities based on their economic potential. The dataset was obtained from the Ministry of Marine Affairs and Fisheries, containing attributes such as commodity name, export volume (tons), and export value (million USD). Data preprocessing involved cleaning, normalization, and clustering using the K-Means algorithm with K=3, which was determined objectively through the Elbow and Silhouette methods. The results yielded three main clusters: superior, potential, and super superior, classified based on average export volume and value. The system was built using the Flask framework and visualized the results through interactive Scatter plots. Evaluation using Silhouette Score (0.62) and Davies–Bouldin Index (0.49) demonstrated that the applied method effectively maps the economic potential of marine commodities. The SILAUTINA system can be utilized as a data-driven decision-making tool for policymakers in strategic marine export planning. Thus, this research not only provides a validated clustering methodology but also delivers an operational platform that can be readily adopted by stakeholders in the maritime sector.
Optimization of Support Vector Machine for the Classification of Nutritional Status in Children Emanuell Deftavalandra Rahmanto; Charitas Fibriani (SCOPUS ID=57192643331)
SISTEMASI Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6192

Abstract

Toddlerhood is a critical developmental period that requires precise nutritional monitoring. However, automated classification systems are often challenged by imbalanced data, which makes minority classes difficult to detect accurately. This study aims to optimize a Support Vector Machine (SVM) using a polynomial kernel to improve detection sensitivity for critical classes. By excluding BMI features to avoid redundancy, the proposed model achieved an accuracy of 98%. The main novelty of this research lies in its achievement of an F1 Macro Score of 0.86, confirming that the model provides balanced and reliable classification performance across all nutritional status categories. These results demonstrate the model’s superiority in identifying the minority classes of Severe Malnutrition and Undernutrition more effectively than previous studies. Therefore, the model is highly recommended as an objective decision support system for the early detection of stunting.
Penerapan Transaksi Berbasis Web Warung Kopi Klotok Noms Salatiga Menggunakan Metode FAST Ariel Rodjana; Charitas Fibriani
SemanTIK : Teknik Informasi Vol. 11 No. 2 (2025): SemanTIK : Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i2.240

Abstract

Perkembangan teknologi informasi telah mendorong pelaku usaha untuk meningkatkan efisiensi transaksi dan kepuasan pelanggan. Warung Kopi Klotok Noms Salatiga sebelumnya masih menggunakan transaksi tunai yang sering menimbulkan antrean panjang dan keterlambatan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem transaksi berbasis web menggunakan metode Framework for the Application of System Thinking (FAST) guna mengurangi antrean, mempercepat proses pemesanan dan pembayaran, serta meningkatkan pengalaman pelanggan. Metode penelitian meliputi investigasi awal, analisis masalah, analisis kebutuhan, perancangan, dan implementasi sistem. Data diperoleh melalui wawancara, observasi, dan dokumentasi dengan pihak manajemen serta staf operasional. Hasil penelitian menunjukkan bahwa aplikasi Noms Order mampu memfasilitasi pelanggan dalam melakukan pemesanan dan pembayaran secara daring, memudahkan kasir dalam mengelola transaksi, serta mendukung pramusaji dalam memperbarui status pesanan. Selain itu, sistem menyediakan laporan penjualan yang lebih terstruktur sehingga mendukung pengambilan keputusan manajerial. Penerapan metode FAST terbukti efektif dalam membangun sistem transaksi berbasis web yang meningkatkan efisiensi operasional, mengurangi antrean, dan memberikan kepuasan lebih tinggi kepada pelanggan. Temuan ini menegaskan pentingnya penggunaan metodologi terstruktur seperti FAST dalam pengembangan sistem informasi bagi usaha kecil dan menengah The rapid development of information technology has encouraged businesses to improve transaction efficiency and customer satisfaction. Warung Kopi Klotok Noms Salatiga previously relied on cash-based transactions, which often caused long queues and delays. This study aims to design and implement a web-based transaction system using the Framework for the Application of System Thinking (FAST) method to reduce queues, accelerate ordering and payment processes, and enhance the overall customer experience. The research method consists of preliminary investigation, problem analysis, requirements analysis, system design, and implementation. Data were collected through interviews, observations, and documentation involving management and operational staff. The results indicate that the Noms Order application enables customers to place orders and make payments online, assists cashiers in managing transactions more efficiently, and supports waiters in updating order status. Furthermore, the system provides structured sales reports that improve managerial decision-making. The implementation of the FAST method proved effective in developing a web-based transaction system that enhances operational efficiency, reduces waiting times, and increases customer satisfaction. These findings highlight the significance of adopting structured methodologies such as FAST in building reliable information systems for small and medium-sized enterprises
The Application of Restful Web Service and Json for Poultry Farm Monitoring System Hindriyanto Dwi Purnomo; Dody Agung Saputro; Ramos Somya; Charitas Fibriani
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 1 No. 1 (2016): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v1i1.183

Abstract

Partnership schema is widely applied in Indonesia poultry farm industry. In this schema, a poultry company cooperates with many breeder partners to raise their chicken. The company sends their field inspection staffs to monitor the growth of the chickens. Large number of breeders with manual process of report, handle, and monitor takes a significant amount of time and efforts. In addition, the data cannot be observed immediately by the company. A poultry farm monitoring system based on the Application Programming Interface (API) is proposed in this research. The system can be used by breeders, breeder partners and field inspection staffs to facilitate the process of reporting, handling and monitoring by the poultry company. The API technology is applied as a data center and a data provider. The combination of RESTful web service and JSON into the API enable the integration can be processed safely as well as simple and easy to use. The proposed system can be applied to complement or replace the existing manual processes on many poultry farms with partnership schema.
Novel Genre Classification based on Synopsis using the Random Forest Algorithm Prananing Mahanani; Charitas Fibriani (SCOPUS ID=57192643331)
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

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.
Classification of Online Game Player Engagement Levels using the Random Forest Algorithm Febrian Joseph Laia; Charitas Fibriani (SCOPUS ID=57192643331)
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6514

Abstract

The rapid growth in the number of online game players has generated large volumes of behavioral data that can be leveraged to analyze player engagement levels. However, the primary challenges include class imbalance in engagement data and the limited interpretability of predictive models regarding the factors influencing their decisions. This study aims to develop a classification model for online game player engagement levels using the Random Forest algorithm, while addressing class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE) and identifying the most influential features using Feature Importance analysis. The study utilized the Online Gaming Behavior Dataset from Kaggle, comprising 40,034 records. The Random Forest model was optimized using RandomizedSearchCV with five-fold cross-validation to determine the optimal hyperparameter configuration. The experimental results demonstrate that the proposed model achieved an overall accuracy of 92%, with recall values of 90%, 94%, and 89% for the Low, Medium, and High engagement classes, respectively. Feature Importance analysis using both impurity-based and permutation approaches consistently identified SessionsPerWeek (0.4375 and 0.4309) and AvgSessionDurationMinutes (0.3371 and 0.3540) as the two most influential features, jointly accounting for more than 77% of the model's predictive decisions, whereas demographic features contributed only marginally. The novelty of this study lies in the integration of Random Forest, SMOTE, and Feature Importance to develop a classification model that is not only highly accurate but also interpretable. These findings provide valuable insights for game developers in designing evidence-based player retention strategies, such as implementing daily login rewards to increase session frequency and time-limited events to encourage longer gameplay sessions.
Analisis Usability eOffice UKSW Menggunakan Technology Acceptance Model Alfero Timothy Mundung; Charitas Fibriani
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

ANALISIS STRATEGI BERSAING PADA PERUSAHAAN PENYEDIA JASA LAYANAN INTERNET: PT. MITRA JARINGAN ANDALAN MENGGUNAKAN PORTER’S FIVE FORCES Hanan Setyawan; Charitas Fibriani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6427

Abstract

Perkembangan bisnis yang semakin cepat menyebabkan kompetisi antar perusahaan semakin ketat. Dalam industri telekomunikasi, PT. Mitra Jaringan Andalan Salatiga menghadapi tantangan untuk mempertahankan dan meningkatkan daya saingnya. Persaingan yang tinggi memerlukan analisis mendalam untuk memahami faktor-faktor yang mempengaruhi daya saing perusahaan. Penting bagi PT. Mitra Jaringan Andalan Salatiga untuk mengidentifikasi strategi bersaing yang efektif guna menghadapi ancaman dari pesaing, produk substitusi, serta kekuatan tawar pembeli dan pemasok. Penelitian ini menggunakan kerangka kerja Porter's Five Forces untuk menganalisis strategi bersaing PT. Mitra Jaringan Andalan Salatiga. Analisis ini mencakup lima aspek utama: ancaman pendatang baru, ancaman produk substitusi, kekuatan tawar pembeli, kekuatan tawar pemasok, dan tingkat persaingan dalam industri. Hasil penelitian memberikan wawasan mendalam mengenai faktor-faktor yang mempengaruhi daya saing PT. Mitra Jaringan Andalan Salatiga. Temuan ini membantu perusahaan dalam merumuskan strategi yang dapat meningkatkan daya saingnya di pasar telekomunikasi yang kompetitif. Strategi yang diusulkan diharapkan dapat meningkatkan efisiensi operasional dan mempertahankan pangsa pasar perusahaan di tengah persaingan yang ketat.
Co-Authors Adenia Kusuma Dayanthi Adriyanto Juliastomo Gundo Adyawangkara Katon Prasidya Alamsyah, Samuel Sandy Alfero Timothy Mundung Andeka Rocky Tanaamah Andhika Dwi Putra Bagaskara Suprapto Suprapto Andrian, Joshua Valens Angela Atik Setiyanti Antar Maramba Jawa Anton Hermawan Antonius Mbay Ndapamury Ardian Ariadi Ariel Rodjana Arya, William Cadwell Marthin Rumagit Chinta, Caroline Amanda Danny Manongga Desy Angeline Dian Novita Kristiyani Dian Widiyanto Chandra Diane Junianti Dimas Wahyu Eko Prasetyo Dody Agung Saputro Eldo, Steven Elvira Umar Emanuell Deftavalandra Rahmanto Endrick Fernando Suhono Erwien Christianto Febrian Joseph Laia Gandhi Alip Wijayanto Gloria Milenda Jesika Prima Gudiato, Candra Halim Yosephine Hanan Setyawan Hanita Yulia HARTONO, DAVID Hartono, Edwin Hendry Hindriyanto Dwi Purnomo Hiskia, Cheryl Louisa Loedwyca Indrastanti Ratna Widiasari Irawati, Susi Juseka, Christien Julio Kieky Adrian Sarwono Krismiyati Kurniawan, Restu Yoga Loppies, Marthin Wilheim Lumban Batu, Juliana Andretha Janet Mayer Jeloe Susilo Perdana Merryana Lestari Mira Mira Octavianus Dicky Wahyudi Panggabean, Albert Jonathan Prananing Mahanani Pratama, Nathan Putra Prima, Genaro Asa Puntoriza Puntoriza Putra, Marcellino Adi Ramba, Rileanly Masakke Ramos Somya Ravensca Matatula Raymond Elias Mauboy Rissal Efendi Sambadagni, Sabella Yasmin Silvia Yolanda Sastanti Somya Ramos Sri Yulianto Joko Prasetyo Stanny Dewanty Rehatta Stevanus Dwi Istiavan Mau Suryana, Steven Immanuel Sutrisno, Ezra Harfi T. Arie Setiawan Prasida Theopillus J. H. Wellem Triloka Mahesti Valentino Kevin Sitanayah Que Vinsensius Aprila Kore Dima Virginia, Mila Kavita Wata, Denis Christo Van Halen William Christanto Willson Mangoki Winsy Weku Winsy Weku Yerik Afrianto Singgalen Yerymia Alfa Susetyo Yessica Nataliani Yohannes Neman, Pius