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All Journal Sisforma: Journal of Information Systems JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab TEMATIKA: Jurnal Penelitian Teknik Informatika dan Sistem Informasi JIKO (Jurnal Informatika dan Komputer) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Sisfokom (Sistem Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JSAI (Journal Scientific and Applied Informatics) E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Pengabdian Kepada Masyarakat Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Sistem Informasi dan Teknologi Informasi Journal of Vocational, Informatics and Computer Education
Phie Chyan
Fakultas Teknologi Informasi, Universitas Atma Jaya Makassar
Arts Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Pelatihan Peningkatan Pemanfaatan dan Literasi Teknologi Informasi dalam Mendukung Mitra UMKM Salon Kecantikan Phie Chyan; Ridnaldy Yunior Carolus
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 4 No 1 (2024): Mei 2024
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v4i1.2673

Abstract

The community services carried out are a service program to support government programs in increasing the competitiveness of MSMEs so that they can compete in today's very tight competitive situation. MSMEs in 3T areas (disadvantaged, frontier, outermost) face much more complex problems than those in relatively more developed areas. Partners in this service activity are the MSME beauty salon HNB Salon & SPA located in Nabire Regency, Central Papua. Based on the analysis carried out by the service team, partners experience problems related to the service sales process and also management, especially in terms of customer relationship management and management of marketing activities, then to help MSME partner, the service team will support the use of technology in administering the services provided at the salon in the form of salon service applications. Additionally, increasing human resource capabilities is also very much needed, especially in using various technological equipment, both hardware and software, to support salon operations. Finally, the service team also provides training on using social media to carry out digital marketing to MSME staff. Based on the evaluation of the material for the training activities, excellent results were obtained, and the partners stated that they were very helped by the material and program that the service team had provided.
Model Dataset Bahan Baku Sampah Organik Berbasis Citra Digital dengan Machine Learning Saptadi, Norbertus Tri Suswanto; Chyan, Phie; Sumarta, Sean Coonery; Cakra, Kalvin
e-Jurnal JUSITI (Jurnal Sistem Informasi dan Teknologi Informasi) Vol. 13 No. 1 (2024): e-Jurnal JUSITI
Publisher : Universitas Dipa Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36774/jusiti.v13i1.1553

Abstract

Kota Makassar memiliki sampah organik yang dapat diolah dan menghasilkan potensi energi yang bersumber dari bahan baku tempurung kelapa. Pemanfaatan sampah organik menjadi bahan baku tempurung kelapa dapat menghasilkan produk briket biomassa. Kualitas bahan baku ditentukan dari sumber asal tempurung kelapa yang berada di sekitar lingkungan masyarakat. Tujuan penelitian adalah merancang model dataset berbasis citra digital dalam upaya mengetahui kualitas tempurung kelapa sebagai bahan baku sampah organik. Penelitian menggunakan metode deep learning sebagai bagian dari machine learning yang dapat mengevaluasi permasalahan deteksi objek dalam klasifikasi citra digital. Hasil penggunaan arsitektur CNN telah menghasilkan perancangan dan pemodelan data dengan nilai akurasi model sebesar 85%.
Image Restoration Using Deep Learning Based Image Completion Chyan, Phie; Saptadi, Tri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1699

Abstract

Digital images can experience various disturbances in acquisition and storage, one of which is a disturbance indicated by damage to certain areas of the image field and causes the loss of some of the information represented by the image. One of the ways to restore an image experiencing disturbances like this is with image completion technology. Image completion is an image restoration technology capable of filling in or completing missing or corrupted parts of an image. Various methods have been developed for this image completion, starting from those based on basic image processing to the latest relying on artificial intelligence algorithms. This study aims to develop and implement an image completion model based on deep learning with the transfer learning method from the completion.net architecture. Using the Facesrub training dataset consisting of a collection of unique facial photos allows the model to understand facial attributes better. Compared to conventional image completion based on image patches, the method developed in this study can perform image filling in image gaps with more realistic results. Based on visual tests conducted on respondents, the results obtained enable respondents to understand all the information represented by the restored image, similar to the original image.
Hybrid Deep Learning Approach For Stress Detection Model Through Speech Signal Chyan, Phie; Achmad, Andani; Nurtanio, Ingrid; Areni, Intan Sari
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2026

Abstract

Stress is a psychological condition that requires proper treatment due to its potential long-term effects on health and cognitive faculties. This is particularly pertinent when considering pre- and early-school-age children, where stress can yield a range of adverse effects. Furthermore, detection in children requires a particular approach different from adults because of their physical and cognitive limitations. Traditional approaches, such as psychological assessments or the measurement of biosignal parameters prove ineffective in this context. Speech is also one of the approaches used to detect stress without causing discomfort to the subject and does not require prerequisites for a certain level of cognitive ability. Therefore, this study introduced a hybrid deep learning approach using supervised and unsupervised learning in a stress detection model. The model predicted the stress state of the subject and provided positional data point analysis in the form of a cluster map to obtain information on the degree using CNN and GSOM algorithms. The results showed an average accuracy and F1 score of 94.7% and 95%, using the children's voice dataset. To compare with the state-of-the-art, model were tested with the open-source DAIC Woz dataset and obtained average accuracy and F1 scores of 89% and 88%. The cluster map generated by GSOM further underscored the discerning capability in identifying stress and quantifying the degree experienced by the subjects, based on their speech patterns
Speedup, Efficiency, and Scalability of the Ray Framework for Audio Feature Extraction in a Single-Node Virtualized Environment: An Empirical Benchmarking Study Phie Chyan; Sean Coonery Sumarta
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.418

Abstract

Purpose – This study aims to evaluate the performance, speedup, efficiency, and scalability of the Ray framework in a single-node virtualized environment for CPU-bound audio feature extraction tasks.Methods – An empirical benchmarking approach was employed using a dataset of 1,000 audio files with durations of 3–5 seconds. Multiple feature extraction techniques, including MFCC, spectral centroid, spectral rolloff, chroma features, and zero-crossing rate, were implemented using the Librosa library. Performance was evaluated by comparing serial and parallel execution times across different worker configurations.Findings – The results show that execution time decreased from 59.62 seconds in serial execution to 9.86 seconds when using 16 parallel workers, achieving a maximum speedup of 5.98. The system exhibits sub-linear scalability, with efficiency decreasing as the number of workers increases due to task scheduling overhead, resource contention, and virtualization constraints. An optimal performance range is observed at 8–12 workers, where significant speedup is achieved with relatively better efficiency.Research implications – This study demonstrates that the Ray framework challenges the assumption of linear scalability in CPU-bound parallel workloads by revealing how system-level constraints in virtualized single-node environments fundamentally shape speedup and efficiency trade-offs.Conclusion – This study demonstrates that the Ray framework is an effective and practical solution for accelerating embarrassingly parallel, CPU-bound workloads in single-node virtualized environments. While performance improves with increasing parallelism, careful selection of the number of workers is necessary to balance speedup and efficiency. However, the findings are limited by the use of a single-node setup and a relatively small dataset, suggesting that further evaluation in larger-scale or distributed environments is needed.
Implementasi dan Analisis Kinerja Komputasi Paralel Menggunakan Ray pada Lingkungan Multi-Core untuk Pemrosesan Audio Phie Chyan; Sean Coonery Sumarta
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9649

Abstract

Parallel computing has become an effective approach to improving data processing efficiency, particularly for CPU-bound applications. This study aims to implement parallel computing using the Ray framework for audio feature extraction in a multi-core single-node environment. The dataset consists of 1000 .wav audio files from the RAVDESS dataset, which are processed through feature extraction in the time, frequency, and time–frequency domains using the Librosa library. The extracted features include zero-crossing rate, spectral features, and Mel-Frequency Cepstral Coefficients (MFCC), which provide a comprehensive representation of audio signal characteristics. Each audio file is treated as an independent task and distributed across multiple workers for parallel processing. This approach allows data processing to be performed independently without inter-task dependencies. Experiments are conducted by varying the number of workers from 1 to 4 to observe their impact on execution time. The primary parameter observed is the total execution time required to complete the feature extraction process. The results show that the implementation of parallel computing reduces execution time compared to serial processing. However, the performance improvement is not strictly linear due to system overhead and hardware resource limitations. These findings indicate that a task parallelism approach using Ray can serve as a practical solution to improve audio data processing efficiency in resource-constrained environments without requiring complex distributed computing infrastructure.
PERANCANGAN LEARNING MANAGEMENT SYSTEM SEBAGAI PENDUKUNG PEMBELAJARAN JARAK JAUH Phie Chyan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 6 No 1 (2021): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v6i1.1521

Abstract

The distance education system (PJJ), which is perceived as a 21st century innovation, is an education system that has broad outreach across space, time and socioeconomics. The PJJ system opens access to education for anyone, anywhere and at any time. In 2018, the gross enrollment rate of higher education in Indonesia will reach 33.3%. still far from achieving the national target, which is 50% by 2024. This research focuses on designing a Learning Management System (LMS) to support the application of distance learning. The LMS that is developed will be able to support the preparation, presupposition and distribution of teaching materials, the process of learning assessment in various forms of learning evaluation models and to support interactions between instructors and students through various forms of interaction using audio visual media. The results obtained are an LMS that is able to meet the needs of teaching and learning in distance and blended learning.
Co-Authors Adi Chandra Syarif Andani Achmad Andrew Christhoper Pratama Areni, Intan Sari Cakra, Kalvin Ferdinandus Sampe Fransiskus Eduardus Daromes Gormantara, Alfredo Hans Christian Marwi Hans Christian Marwi, Hans Christian Heri Heri Heri, Heri Indra Wihanry Indra Wihanry, Indra Ingrid Nurtanio Jeremias Leda Leda, Jeremias Mathias Norbertus Tri Suswanto Saptadi Ridnaldy Yunior Carolus Sean Coonery Sumarta Sean Coonery Sumarta Sean Coonery Sumarta Sean Coonery Sumarta, Sean Coonery Sumarta, Sean Coonery Valentina Marchella Widjaja Yudi Kasmara