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All Journal ComEngApp : Computer Engineering and Applications Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology) Sisforma: Journal of Information Systems Journal of Information Systems Engineering and Business Intelligence EMITTER International Journal of Engineering Technology IJoICT (International Journal on Information and Communication Technology) E-Dimas: Jurnal Pengabdian kepada Masyarakat Fountain of Informatics Journal Journal of Information Technology and Computer Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING JMM (Jurnal Masyarakat Mandiri) JCES (Journal of Character Education Society) JUTEI (Jurnal Terapan Teknologi Informasi) International Journal of New Media Technology ABDIMAS SILIWANGI Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Journal of Information Technology and Computer Engineering ComTech: Computer, Mathematics and Engineering Applications Altruis: Journal of Community Services Jurnal Abdimas Ilmiah Citra Bakti (JAICB) Journal of Technology and Informatics (JoTI) Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Inovatif Wira Wacana JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Oscar Karnalim, Oscar
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Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Prediksi Analisis Sentimen Data Debat Pemilihan Presiden 2024 Menggunakan Support Vector Machine (SVM) Vardina Nava Madya Kusman; Vanessa Metayani; Oscar Karnalim

Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v16i1.4887

Abstract

Penelitian ini bertujuan untuk mengembangkan model klasifikasi menggunakan Support Vector Machine untuk menganalisis sentimen pada data dialog debat Pemilihan Presiden tahun 2024. Sentimen dari ucapan tidak selalu dapat diketahui, sehingga model dalam penelitian ini diusulkan untuk menemukan sentimen dibalik ucapan. Untuk dapat memprediksi sentimen, model dilatih dengan data debat pilpres yang telah dikumpulkan. Model kemudian melakukan klasifikasi terhadap data tersebut, dan kemudian diuji tingkat akurasinya. Setelah diuji menggunakan data tes, diperoleh nilai akurasi sebesar 52,5%. Hasil tersebut kurang memuaskan, maka dilakukan optimasi terhadap model dan data, Hasilnya, nilai akurasi meningkat menjadi sekitar 94%. Untuk kedepannya, mungkin data yang digunakan bisa semakin ditingkatkan dengan memperhatikan distribusi kelas dalam data.
Chat GPT Impact Analysis on API Testing: A Controlled Experiment Setiawan, Yehezkiel David; Yudha, Laurentius Gusti Ontoseno Panata; Mulyono, Yovie Adhisti; Simalango, Veronica Marcella Angela; Karnalim, Oscar
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8182

Abstract

This research examines the impact of ChatGPT as a learning aid for students in API testing. A controlled experiment compared two groups: one utilizing ChatGPT and the other relying on traditional documentation. The findings indicate that participants using ChatGPT scored significantly higher in both exam tests compared to the documentation group, despite taking longer to complete tasks. Statistical analysis using t-tests confirmed these differences as significant. Post-test surveys revealed an increase in participants confidence and effectiveness in understanding and using APIs after interacting with ChatGPT. However, potential downsides, such as over-reliance on ChatGPT and insufficient deep conceptual understanding, were also observed. The results suggest that while ChatGPT can greatly enhance the quality of learning and productivity in API-related tasks, users must balance AI assistance with independent problem-solving skills. This study underscores the potential of ChatGPT as a valuable educational tool, provided it is integrated thoughtfully into the learning process.
Introducing a Practical Educational Tool for Correlating Algorithm Time Complexity with Real Program Execution Kurniawati, Gisela; Karnalim, Oscar
Journal of Information Technology and Computer Science Vol. 3 No. 1: June 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (714.019 KB) | DOI: 10.25126/jitecs.20183140

Abstract

Algorithm time complexity is an important topic to be learned for programmer; it could define whether an algorithm is practical to be used on real environment or not. However, learning such material is not a trivial task. Based on our informal observation regarding students’ test, most of them could not correlate Big-Oh equation to real program execution. This paper proposes JCEL, an educational tool that acts as a supportive tool for learning algorithm time complexity. Using this tool, user could learn how to correlate Big-Oh equation with real program execution by providing three components: a Java source code, source code input set, and time complexity equations. According to our evaluation, students feel that JCEL is helpful for learning the correlation between Big-Oh equation and real program execution. Further, the use of Pearson correlation in JCEL shows a promising result.
Initial Suspicion on Detecting Code Plagiarism and Collusion in Academia: Case Study of Algorithm and Data Structure Courses Ayub, Mewati; Karnalim, Oscar; Wijanto, Maresha Caroline; Risal, Risal
Journal of Information Technology and Computer Science Vol. 6 No. 1: April 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (947.087 KB) | DOI: 10.25126/jitecs.202161274

Abstract

In engineering education, some assessments require the students to submit program code, and since that code might be a result of plagiarism or collusion, a similarity detection tool is often used to filter excessively similar programs. To improve the scalability of such a tool, it is suggested to initially suspect some programs and only compare those programs to others (instead of exhaustively compare all programs one another). This paper compares the ef-fectiveness of two common techniques to raise such initial suspicion: focusing on the submissions of smart students (as they are likely to be copied), or the submissions of slow-paced students (since those students are likely to breach academic integrity to get higher assessment mark). Our study shows that the latter statistically outperforms the former by 13% in terms of precision; slow-paced students are likely to be the perpetrators, but they fail to get the submissions of smart students.
Dynamic Sign Language Recognition in Bahasa using MediaPipe, Long Short-Term Memory, and Convolutional Neural Network Lemmuela , Ivana Valentina; Ayub, Mewati; Karnalim, Oscar
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 1 (2025): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.1.17-29

Abstract

Background: Communication is important for everyone, including individuals with hearing and speech impairments. For this demographic, sign language is widely used as the primary medium of communication with others who share similar conditions or with hearing individuals who understand sign language. However, communication difficulties arise when individuals with these impairments attempt to interact with those who do not understand sign language. Objective: This research aims to develop models capable of recognizing sign language movements in Bahasa and converting the detected gesture into corresponding words, with a focus on vocabularies related to religious activities. Specifically, the research examined dynamic sign language in Bahasa, which comprised gestures requiring motion for proper demonstration. Methods: In accordance with the research objective, sign language recognition model was developed using MediaPipe-assisted extraction process. Recognition of dynamic sign language in Bahasa was achieved through the application of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) methods. Results: Sign language recognition model developed using bidirectional LSTM showed the best result with a testing accuracy of 100%. However, the best result for the CNN alone was 86.67 %. The integration of CNN and LSTM was observed to improve performance than CNN alone, with the best CNN-LSTM model achieving an accuracy of 95.24%. Conclusion: The bidirectional LSTM model outperformed the unidirectional LSTM by capturing richer temporal information, with a specific consideration of both past and future time steps. Based on the observations made, CNN alone could not match the effectiveness of the Bidirectional LSTM, but a combination of CNN with LSTM produced better results. It is also important to state that normalized landmark data was found to significantly improve accuracy. Accuracy within this context was also influenced by shot type variability and specific landmark coordinates. Furthermore, the dataset containing straight-shot videos with x and y coordinates provided more accurate results, dissimilar to those comprised of videos with shot variation, which typically require x, y, and z coordinates for optimal accuracy. Keywords: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), MediaPipe, Sign Language
Extended Vector Space Model with Semantic Relatedness on Java Archive Search Engine Oscar Karnalim
Jurnal Teknik Informatika dan Sistem Informasi Vol 1 No 2 (2015): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v1i2.578

Abstract

Byte code as information source is a novel approach which enable Java archive search engine to be built without relying on another resources except the Java archive itself [1]. Unfortunately, its effectiveness is not considerably high since some relevant documents may not be retrieved because of vocabulary mismatch. In this research, a vector space model (VSM) is extended with semantic relatedness to overcome vocabulary mismatch issue in Java archive search engine. Aiming the most effective retrieval model, some sort of equations in retrieval models are also proposed and evaluated such as sum up all related term, substituting non-existing term with most related term, logaritmic normalization, context-specific relatedness, and low-rank query-related retrieved documents. In general, semantic relatedness improves recall as a tradeoff of its precision reduction. We also proposed a scheme to take the advantage of relatedness without affected by its disadvantage (VSM + considering non-retrieved documents as low-rank retrieved documents using semantic relatedness). This scheme assures that relatedness score should be ranked lower than standard exact-match score. This scheme yields 1.754% higher effectiveness than our standard VSM.
AP-ASD1 : An Indonesian Desktop-based Educational Tool for Basic Data Structure Course Lucky Christiawan; Oscar Karnalim
Jurnal Teknik Informatika dan Sistem Informasi Vol 2 No 1 (2016): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v2i1.605

Abstract

Although there are so many avalaible data structure educational tools, it is quite difficult to find a suitable tool to aid students for learning certain course [1]. Several major impediments in determining the tool are teaching preferences, language barrier, confusing terminologies, internet dependency, various degree of material difficulty, and other environment aspects. In this research, a data structure educational tool called AP-ASD1 is developed based on basic algorithm and data structure course (ASD 1). Since AP-ASD1 is developed following course materials and not vice versa, this educational tool is guaranteed to fit in our needs. The feasibility of AP-ASD1 is evaluated based on two factors which are functionality correctness and survey. All features are correctly functioned and yield expected output whereas survey yields fairly good result (84,305% achievement rate). Based on our survey, AP-ASD1 meets eligibility standard and its features are also successfully integrated. Survey also concludes that this application is also quite effective as a supportive tool for learning basic data structure.
Advisor-Oriented Course Recommendation System Using Student Grades Pangestu, Muftah Afrizal; Karnalim, Oscar
JITCE (Journal of Information Technology and Computer Engineering) Vol. 7 No. 2 (2023)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.2.63-72.2023

Abstract

In some universities, student advisors are often hired to enhance students’ retention rate. Having some students in mind, these advisors may find some difficulties in guiding the students in terms of selecting relevant courses. This paper proposes an advisor-oriented course recommendation system. Using this system, the advisors may suggest relevant courses to their students easier and more accurate. This system relies on student grades and comprehensive course data. Further, it utilises content-based and collaborative filtering for predicting relevant courses. According to our evaluation, the system is considerably effective; the accuracy of content-based filtering is about 66% while the accuracy of collaborative filtering is about 58%. Further, some parameters may be potential for enhancing accuracy while the others may be not.
PELATIHAN GURU DAN TANTANGAN BEBRAS 2024 UNTUK PENGENALAN COMPUTATIONAL THINKING DI BIRO BEBRAS MARANATHA Wijanto, Maresha Caroline; Toba, Hapnes; Ayub, Mewati; Karnalim, Oscar; Tan, Robby; Natasya, Rossevine Artha; Senjaya, Wenny Franciska; Adelia; Edi, Doro; Bunyamin, Hendra; Kasih, Julianti; Yulianti, Diana Trivena; Widjaja, Andreas; Johan, Meliana Christianti; Surjawan, Daniel Jahja; Zakaria, Teddy Marcus; Risal; Kandaga, Tjatur
Jurnal Abdimas Ilmiah Citra Bakti Vol. 6 No. 2 (2025)
Publisher : STKIP Citra Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38048/jailcb.v6i2.5237

Abstract

Pemahaman siswa terhadap konsep Computational Thinking (CT) masih tergolong rendah, sementara pengenalan terhadap CT menjadi krusial di era digital saat ini. Tantangan Bebras menjadi sarana edukatif yang efektif untuk memperkenalkan CT melalui berbagai soal (Bebras task) yang bersifat aplikatif dan menantang. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterlibatan siswa dalam CT melalui pembekalan guru dan pelaksanaan Tantangan Bebras 2024. Mitra kegiatan adalah guru dan siswa dari jenjang SD, SMP, dan SMA yang tergabung dalam Biro Bebras Maranatha. Metode yang digunakan meliputi lokakarya nasional, pelatihan guru, technical meeting, pelaksanaan Tantangan Bebras, dan evaluasi prestasi siswa. Hasil menunjukkan peningkatan partisipasi peserta sebanyak 4.429 siswa dari 136 sekolah, meningkat signifikan dibanding tahun sebelumnya. Sebanyak 165 siswa berhasil meraih peringkat 1–6, dengan sebagian besar berasal dari sekolah yang mengikuti Gerakan PANDAI. Evaluasi juga menunjukkan bahwa pembekalan guru efektif meningkatkan kesiapan dalam mengenalkan CT kepada siswa. Kegiatan ini menunjukkan bahwa kolaborasi antara pelatihan guru dan Tantangan Bebras dapat menjadi strategi efektif untuk memperluas pemahaman dan kemampuan siswa dalam CT.
Pemanfaatan Machine Learning dalam Prediksi Rating: Studi Kasus pada Data Abstrak Publikasi Ilmiah Zaqi Megantara, Rizky; Iryanto Faot, Pace; Haba Ito, Ridolof; Felicia Annabel, Kathleen; Karnalim, Oscar
Journal of Technology and Informatics (JoTI) Vol. 7 No. 1 (2025): Vol. 7 No.1 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i1.999

Abstract

As the volume of scientific publications increases, the need for automated approaches to evaluate and analyze abstracts becomes increasingly important. This research not only aims to predict the abstract rating of scientific publications using machine learning algorithms, but also offers a unique approach by integrating regression and classification analysis to evaluate the relevance of abstracts more comprehensively. Four main models, namely XGBoost Regressor, Random Forest Regressor, Support Vector Regressor (SVR), and K-Nearest Neighbors Regressor (KNN), are evaluated for this task. The dataset is processed through preprocessing stages which include removing duplications, text representation using TF-IDF, handling data imbalances with Synthetic Minority Oversampling Technique (SMOTE), and dimension reduction using Truncated Singular Value Decomposition (SVD). The research results show that SVR provides performance the best with the lowest Mean Absolute Error (MAE) value of 0.4980, Mean Squared Error (MSE) of 0.5237, and the highest R² of 0.7321. XGBoost and Random Forest show competitive performance with advantages in computational efficiency and prediction stability respectively, while KNN provides varying results depending on the data distribution. Dimensionality reduction using Truncated SVD successfully preserves more than 70% of the initial variance, enabling higher computational efficiency without losing important information. This research makes a significant contribution in supporting machine learning-based decision making, especially in the analysis of abstracts of scientific publications. This approach can be further developed through exploration of ensemble or hybrid models, as well as testing on larger datasets to improve generalization and accuracy.
Co-Authors ADELIA Adelia Adelia, Adelia Aditya Permadi Aditya Permadi Aldi Aldiansyah Andreas Widjaja Andreas Widjaja Andrisyah Andrisyah Andrisyah Andrisyah Annabel, Kathleen Felicia Avinash, Avinash Aziz Mu’min Bayu Rima Aditya Bertha Alan Manuel Bertha Alan Manuel Daniel Jahja Surjawan Devion Tanrico Diana Trivena Yulianti Dina Fitria Murad Dina Fitria Murad Doro Edi Egie Imandha, Egie Elvina Elvina Elvina Elvina Erico Darmawan Handoyo Fathul Jannah Felicia Annabel, Kathleen Felix Christian Jonathan Felix Christian Jonathan Felix Christian Jonathan Gisela Kurniawati Haba Ito, Ridolof Hapnes Toba Hendra Bunyamin Hendra Bunyamin Hendra Bunyamin Irawan Nurhas Iryanto Faot, Pace Ivana Valentina Johan, Meliana Christianti Julianti Kasih Julianti Kasih, Julianti Kurniawan, Phin Kurniawati, Gisela Kusman, Vardina Nava Madya Lemmuela , Ivana Valentina Liliawati, Swat Lie Lisan Sulistiani Lucky Christiawan Lucky Christiawan, Lucky Majiah, Arya Tri Putra Maresha Caroline Wijanto Maresha Caroline Wijanto Maresha Caroline Wijanto Maresha Caroline Wijanto Maresha Caroline Wijanto Maresha Caroline Wijanto Marlina Marlina Martua, Juan Sterling Metayani, Vanessa Mewati Ayub Mulyono, Yovie Adhisti Mu’min, Aziz Oscar Wongso Pangestu, Muftah Afrizal Panji Yudasetya Wiwaha Rachmi Rachmadiany Ricardo Franclinton Risal Risal Risal Robby Tan Rossevine Artha Nathasya Ruis, Nisa Deviani Agustin Samosir, Moses Marzuki Santiadi, Sherly Sendy Ferdian Sujadi Setia Budi Setia Budi Setiawan, Yehezkiel David Simalango, Veronica Marcella Angela Sofriesilero Zumaytis Sulaeman Santoso Sulistiani, Lisan Tanrico, Devion Teddy Marcus Zakaria Teddy Marcus Zakaria Tendy Cahyadi, Tendy Tjatur Kandaga Valentina, Ivana Vanessa Metayani Vardina Nava Madya Kusman Vincent Elbert Budiman Wenny Franciska Senjaya Wijaya, Bernadus Indra Wiwaha, Panji Yudasetya Yan Sen Paulus Yudha, Laurentius Gusti Ontoseno Panata Zaqi Megantara, Rizky