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All Journal Jurnal Dimensi Pratama Teknik Sipil Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Mantik Journal of Applied Data Sciences Jurnal AbdiMas Nusa Mandiri Indonesian Journal of Chemical Science
Kevin Wijaya, Kevin
Jurusan Teknologi Informasi, Fakultas Teknik, Universitas Udayana
Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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KINERJA SISTEM TUNGGAL STRUKTUR BAJA RANGKA TERBREIS EKSENTRIS BERBENTUK V TERBALIK PADA MID RISE BUILDIN Wijaya, Kevin; Ongkowidjojo, Alberto Orson; Tanojo, Effendy; Santoso, Hasan
Jurnal Dimensi Pratama Teknik Sipil Vol 7, No 2 (2018): AGUSTUS 2018
Publisher : Jurnal Dimensi Pratama Teknik Sipil

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (369.664 KB)

Abstract

Indonesia merupakan salah satu negara yang rawan terkena gempa, sehingga bangunan di Indonesia harus didesain tahan terhadap gempa. Dalam penelitian ini, bangunan didesain menggunakan sistem tunggal dengan menggunakan Rangka Terbreis Eksentris (RTE). Sistem tunggal memilki batasan ketinggian dalam SNI 1726:2012 tabel 9, untuk Kategori Desain Seismik D adalah 48 m (12 lantai). Namun, batasan ketinggian tersebut boleh ditinggikan menjadi 72 m (18 lantai) dengan mengikuti persyaratan sesuai dengan SNI 1726:2012 Pasal 7.2.5.4. Oleh karena itu, penelitian ini dilakukan menggunakan mid-rise building, yaitu 12,15, dan 18 lantai, dengan bresing bentuk V terbalik dan dengan menggunakan dua jenis bentang yaitu bangunan 3 bentang dan bangunan 5 bentang. Hasil penelitian menunjukkan seluruh bangunan mengalami kegagalan pada balok link, ini seusai dengan hirarki desain daripada RTE. Dengan semakin tinggi bangunan maka performa bangunan semakin buruk. Ini bisa dilihat dari hasil displacement dan drift ratio. Pada bangunan 3 bentang menghasilkan nilai displacement yang lebih baik namun menghasilkan menghasilkan nilai drift ratio yang lebih buruk dari bangunan 5 bentang. Namun seluruh bangunan masih dalam satu kategori klasifikasi kerusakan bangunan yaitu collapse prevention. Sendi plastis yang terjadi pada semua bangunan menghasilkan lokasi dan jumlah yang relatif sama sehingga disimpulkan seluruh bangunan memiliki kinerja yang relatif sama.
Sentiment Analysis of ChatGPT on Indonesian Text using Hybrid CNN and Bi-LSTM Prasetyo, Vincentius Riandaru; Naufal, Mohammad Farid; Wijaya, Kevin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6334

Abstract

This study explores sentiment analysis on Indonesian text using a hybrid deep learning approach that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). Due to the complex linguistic structure of the Indonesian language, sentiment classification remains challenging, necessitating advanced methods to capture both local patterns and sequential dependencies. The primary objective of this research is to improve sentiment classification accuracy by leveraging a hybrid model that integrates CNN for feature extraction and Bi-LSTM for contextual understanding. The dataset consists of 800 manually labeled samples collected from social media platforms, preprocessed using case folding, stop word removal, and lemmatization. Word embeddings are generated using the Word2Vec CBOW model, and the classification model is trained using a hybrid architecture. The best performance was achieved with 32 Bi-LSTM units, a dropout rate 0.5, and L2 regularization, which was evaluated using Stratified K-Fold cross-validation. Experimental results demonstrate that the hybrid model outperforms conventional deep learning approaches, achieving 95.24% accuracy, 95.09% precision, 95.15% recall, and 95.99% F1 score. These findings highlight the effectiveness of hybrid architectures in sentiment analysis for low-resource languages. Future work may explore larger datasets or transfer learning to enhance generalizability.
implications of adding chitosan and glycerol to edible film from sweet potato starch on tensile strength Wijaya, Kevin; Tantra Diwa Larasati
Indonesian Journal of Chemical Science Vol. 14 No. 2 (2025): Indonesian Journal of Chemical Science
Publisher : Prodi Kimia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ijcs.v14i2.6906

Abstract

The use of inappropriate plastic in food packaging can pose risks to human health, including the potential for cancer, because the plastic raw materials come from natural gas and petroleum. Even though it is still used, the use of this material is starting to be reduced because its impact is not environmentally friendly and has the potential to be detrimental to health. One innovation that can be implemented is the use of edible organic plastic, such as edible film. The development of edible film materials involves polymer compounds from plants, such as starch. This research aims to find the best formulation for making edible film using various variations of chitosan addition. Edible film is made from sweet potato starch with the addition of 3% variable composition chitosan; 4%; 5% (w/v) and glycerol (3%). Tests show that the higher the chitosan content in starch, the Tensile Strength (TS) of edible film will increase. Findings from the research show that the best edible film is produced from sweet potato starch with a chitosan variation of 5% (w/v).
STRATEGI KEUANGAN UNTUK SISWA SMK: MENYIAPKAN FONDASI KEUANGAN SEJAK DINI Amelinda, Rita; Tanudjaja, Fahrentz Antonio; Onassis, Gabrielle; Ardiyansah, Rio; Erlina, Annastassia; Wijaya, Kevin
Jurnal AbdiMas Nusa Mandiri Vol. 8 No. 1 (2026): Periode Januari 2026
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/abdimas.v8i1.6613

Abstract

Financial planning education is very important including for Vocational High School (SMK) students to prepare for a better future. This topic was chosen due to students' low awareness of the importance of early financial management, which can lead to financial difficulties in the future. This community service programme aims to increase understanding and practical knowledge of financial planning through interactive and applicable methods, such as seminars, questions and answers, and simple budgeting simulations. The results of the activity showed an increase in students' understanding of basic financial concepts, such as saving, managing expenses, and planning small investments. In addition, students were able to develop a more structured and realistic personal budget. The programme succeeded in raising awareness of the importance of financial literacy as a foundation for economic independence. These results confirm that financial planning education at the SMK level can be an important step towards building a financially savvy younger generation.
Career Adaptability And Career Anxiety In Job-Seeking Individuals With Disabilities: The Mediating Role Of Future Time Perspective Wijaya, Kevin; Barkah, Barkah; Jaya, Arman; Rosnani, Titik; Irdhayanti, Efa
Jurnal Mantik Vol. 9 No. 4 (2026): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v9i4.7050

Abstract

This study examined the relationship between career adaptability and career anxiety among job-seeking individuals with disabilities in Indonesia and tested the mediating role of Future Time Perspective (FTP) within the frameworks of Career Construction Theory and Social Cognitive Career Theory. Survey data from 120 respondents were analyzed using Partial Least Squares Structural Equation Modeling. Results show that career adaptability significantly reduces career anxiety and strongly predicts FTP. However, FTP neither predicts career anxiety nor mediates the relationship between adaptability and anxiety. These findings suggest that under structural labor-market constraints, adaptability functions primarily as a direct self-regulatory resource rather than through future-oriented cognition. The study highlights contextual limits of temporal cognition models and underscores the importance of strengthening adaptability in career interventions for individuals with disabilities
Applying Transfer Learning on Various GNN Model Training in Indoor Positioning System Tasks Wijaya, Kevin; Buana, Hanif Muhammad Sangga; Kusuma, Gede Putra
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1150

Abstract

Determining location and orientation has always been a fundamental challenge, driving advances from maps and compasses to modern global navigation satellite systems (GNSS). However, GNSS performs poorly indoors due to signal attenuation and lack of elevation accuracy, necessitating the development of indoor positioning systems (IPS). Various technologies such as Wi-Fi, Bluetooth Low Energy (BLE), and RFID have been deployed, typically relying on received signal strength (RSS) and fingerprinting to improve accuracy. While previous research focused on training a single model for an entire building, this study explores the creation of floor-specific models by applying transfer learning to various GNN models. This is done to address the substantial signal distortion between floors. Using the UTSIndoorLoc dataset, we evaluate Graph Attention Network (GAT), GraphSAGE, and Graph Convolutional Network (GraphConv) for predicting two-dimensional indoor positions based on RSSI fingerprints. We propose 2 transfer learning model training methods, Schema A and Schema B. Schema A trains the base model iteratively through each floor, and Schema B trains the base model on a unified dataset. Schema B with GraphConv achieved the best results with a mean positioning error of 6.2176 meters. Whilst Schema A achieved a best-case mean positioning error of 6.3900 meters. Both outperforming the standard unified model which has a mean positioning error of 8.0808 meters.
Co-Authors Amelinda, Rita Ardiyansah, Rio Arman Jaya Barkah, Barkah Buana, Hanif Muhammad Sangga Effendy Tanojo Erlina, Annastassia Hasan Santoso Irdhayanti, Efa Kusuma, Gede Putra Mohammad Farid Naufal, Mohammad Farid Onassis, Gabrielle Ongkowidjojo, Alberto Orson Tantra Diwa Larasati Tanudjaja, Fahrentz Antonio Titik Rosnani Vincentius Riandaru Prasetyo