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Tri A. Sundara
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tri.sundara@stmikindonesia.ac.id
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INDONESIA
The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 37 Documents
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Search results for , issue "Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science" : 37 Documents clear
The Influence of Tutorial-Based Learning Model on Information and Communication Technology (ICT) Subjects at Junior High School Painan Helmiza; Ganefri; Ambiyar; Irfan, Dedy
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3242

Abstract

This study aims to determine the effect of learning outcomes using a tutorial-based learning model in Information and Communication Technology (ICT) subjects at Junior High School 02 Painan. This study uses a quasi-experimental research method. The population in this study were students of class VIII Junior High School 02 Painan. The sample in this study used a random sampling technique with students in class VIII A as the experimental class and VIII B as the control class with 30 students in class A and 25 students in class B. The research instrument used was objective questions which were tested for validity, reliability, discriminatory power, and level of difficulty. Data were analyzed with parametric statistics including normality test, homogeneity test and hypothesis testing. As well as to test the activity and response of the research instrument used was a questionnaire which was validated by three lecturers who are experts in learning evaluation and the results of effectiveness were analyzed based on classical completeness and based on the Gain Score. The results of this study revealed that student learning outcomes using the tutorial-based learning model with an average score of the experimental class was 78.933 better than ordinary learning with an average score of the control class was 69.76 in the final test analysis where the tcount was 9.484 and ttable is 1.68 with a significance level of 95% so that tcount > ttable and the research hypothesis is accepted. As well as seen from the activeness and response of students, the average result of activity is 80.9% with a very good classification and for the average student response result is 87.4% with a very good classification result. The effectiveness of learning using a tutorial-based learning model in the effectiveness trial, obtained 87% of students scored ≥ 70 which stated classically effectiveness and for the control class obtained 43.33% of students scored < 70 which stated that it was not classically effective. And in terms of Gain Score gets a value of 37.31% (medium). Thus it can be concluded that there is an influence of the tutorial-based learning model on Information and Communication Technology (ICT) learning on student learning outcomes.
Integrating Financial Management and Gamification: A Systematic Literature Review and Future Research Agenda Prasetyo, Amrisandha Pranantya; Santoso, Harry Budi; Putra, Panca Oktavia Hadi
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3244

Abstract

The implementation of gamification in PFM apps has the potential to motivate users to engage in positive financial behaviors. However, several research gaps corroborate the need to investigate gamification in the financial domain further. To address these gaps, this systematic literature review comprehensively examines current research on financial behavior and gamification. This review utilizes the Theory, Context, Characteristics, and Methodology (TCCM) framework to provide a holistic understanding of financial behavior and gamification-related behavioral intention. A total of 53 articles published between 2018 and 2022 were analyzed to assess the present research landscape and provide directions for future studies. This review makes three key contributions: (1) synthesizes current research on financial behavior and gamification-related behavioral intention, (2) presents integrated conceptual models that elucidate financial behavior and gamification-related behavioral intention, and (3) identifies research gaps and suggests avenues for future investigation.
The Pengembangan Media Pembelajaran Berbasis Multimedia Interaktif pada Mata Pelajaran Pemeliharaan Sistem Kelistrikan di Sekolah Menengah Kejuruan Nofiandri, Ernal; Maksum, Hasan; Purwanto, Wawan; Wulansari, Rizky Ema; Muhibbudin
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3247

Abstract

Pada era revolusi 4.0 dengan sangat canggihnya perkembangan teknologi harus dapat dimanfaatkan untuk sebuah kemajuan yang terutama dalam bidang pendidikan. Dari kemudahan yang diberikan oleh perkembangan TIK pendidik harus mampu melihat peluang untuk dapat memanfaatkan Perkembangan TIK untuk memudahkan siswa dalam proses pembelajaran agar dapat meningkatkan keaktifan dan motivasi belajar siswa agar siswa lebih tertarik untuk belajar sehingga meningkatkan hasil belajar dan prestasi belajar siswa terutama pada Sekolah Menengah Kejuruan yang banyak pembelajaran produktif. Berdasarkan hasil observasi dan wawancara diketahui bahwa pembelajaran proses pembelajaran Pemeliharaan Sistem Kelistrikan Kendaraan Ringan bagi sebagian siswa sulit memahami pembelajaran, media pembelajaran yang monoton dan kurangnyas sumber belajar yang interaktif yang membuat rendahnya minat dan motivasi belajar menjadikan hasil belajar siswa rendah sehingga diperlukanya media pembelajaran interaktif yang menyenangkan yang membantu dalam proses pembelajaran. Penelitian ini bertujuan untuk mengembangkan media pembelajaran berbasis multimedia interaktif pada pembelajaran Pemeliharaan Sistem Kelistrikan yang layak dan memenuhi standar aspek validitas pada media dan materi. Metode penelitian yang digunakan adalah pengembangan (R&D) model 4-D, meliputi tahap Define, Design, Develop dan Disseminate, namun tahap pada penelitian ini tahapan Disseminate tidak dilakukan karena hanya melihat validitas dari media pembelajaran multimedia interaktif. Hasil penelitian menunjukkan kevalidan dalam validasi media pembelajaran dengan Validator 1 sebesar 92% dan Validator 2 sebesar 95% dengan rata-rata sebesar 94% kategori sangat valid. Pada validasi materi didapatkan hasil pada Validator 1 82 % dan Validator 2 95% dengan rata-rata 88% kategori sangat valid. Berdasarkan hasil total dari penilaian validitas media dan materi sebesar 91 % yang dimana termasuk dalam kategori Valid atau sangat layak digunakan oleh siswa dalam proses pembelajaran Pemeliharaan Sistem Kelistrikan Kendaraan Ringan.
Probability Prediction for Graduate Admission Using CNN-LSTM Hybrid Algorithm Zuhri, Burhanudin; Harani, Nisa Hanum; Prianto, Cahyo
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3248

Abstract

Currently, the prediction of student admissions still uses conventional machine learning algorithms where there is no algorithm for optimization. This study aims to produce a model that can predict student acceptance of ownership more optimally by using an optimization hybrid learning algorithm, namely the Convolutional Neural Network Long Short Term Memory (CNN-LSTM). This study uses the Microsoft Team Data Science Process method which consists of business understanding, data acquisition & understanding, modeling, and implementation as well as using the acceptance dataset obtained from the kaggle.com website as much as 500 data. The results showed that the CNN-LSTM hybrid learning model could optimize the prediction of students' chances of success in exposure as evidenced by the evaluation results of RMSE of 6.31%, MAE of 4.4%, and R2 of 80.52%. This model is implemented in a website application using the Python language, the Django framework, and the MySQL database.
Performance Analysis and Development of QnA Chatbot Model Using LSTM in Answering Questions Ilyas Tri Khaqiqi, M; Harani, Nisa Hanum; Prianto, Cahyo
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3249

Abstract

This research aims to evaluate the performance of a Long Short-Term Memory (LSTM) based chatbot in answering questions (QnA). LSTM is a type of Recurrent Neural Network (RNN) architecture specifically designed to overcome vanishing gradient problems and can store long-term information. The method used is 5-fold cross-validation to train the chatbot model with 15 epochs at each fold using the dataset provided. The results showed variations in model performance at each fold. At the 5th fold, there was a decrease in performance with 84.63% accuracy, 96.36% precision, 64.9% recall, and 69.84% loss value. This finding shows that there is variability in the performance of the QnA chatbot model at each fold. In conclusion, the LSTM chatbot model can provide good answers with high accuracy and precision. Still, performance variations need to be considered in the use of this chatbot.
Social Media-Based Sentiment Analysis: Electric Vehicle Usage in Indonesia Salsabila, Helmi; Habibi, Roni; Harani, Nisa Hanum
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3250

Abstract

This research analyzes the sentiment regarding the use of electric vehicles in Indonesia through social media, utilizing over 10,000 data points from Twitter. The results indicate a variation of positive, negative, and neutral sentiments towards electric vehicles on social media. Female users play a significant role in expressing their views and actively participating in discussions related to electric vehicles. The locations with the highest user activity discussing electric vehicles are Indonesia, DKI Jakarta, Makassar, Tangerang, and Karawang. The peak activity was observed in September 2019, suggesting a significant interest in electric vehicles. The SVM algorithm achieved an accuracy of 88% for positive and neutral sentiments, but performed relatively lower for negative sentiments. This research lacks data on the gender and age of the respondents. Future studies should address these shortcomings to gain a deeper understanding of public perceptions regarding electric vehicles in Indonesia.
Hyperparameter Tuning Algoritma Supervised Learning untuk Klasifikasi Keluarga Penerima Bantuan Pangan Beras Joshua Agung Nurcahyo; Theopilus Bayu Sasongko
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3254

Abstract

Indonesia memiliki berbagai macam program untuk menekan kemiskinan, salah satunya adalah program bantuan pangan beras. Namun, berdasarkan temuan di lapangan, program bantuan ini tidak tepat sasaran. Melalui klasifikasi supervised learning dengan hyperparameter tuning, penelitian ini bertujuan untuk mengetahui algoritma klasifikasi umum yang paling optimal dan akurat dalam menentukan keluarga penerima bantuan pangan beras. Algoritma Support Vector Machine (SVM), decision tree, naïve bayes, dan K-nearest neighbor (Knn) serta metode hyperparameter tuning grid search, random search, dan optimasi bayesian digunakan dalam penelitian. Data pada penelitian ini bersumber dari IFLS. Berdasarkan hasil analisis, penerapan hyperparameter tuning memiliki dampak yang signifikan dalam meningkatkan kinerja algoritma KNN, decision tree, dan SVM. Algoritma Knn dengan random search serta optimasi bayesian dan SVM dengan optimasi bayesian memberikan nilai akurasi yang sama, yakni sebesar 74%.Oleh karena itu, model tersebut memiliki kinerja yang setara dan sama baiknya dalam mengklasifikasikan keluarga penerima bantuan pangan beras.

Page 4 of 4 | Total Record : 37

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