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Tri A. Sundara
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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 1,127 Documents
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Implementasi Algoritma MFCC dan CNN dalam Klasifikasi Makna Tangisan Bayi Yusdiantoro, Senli Yusdiantoro; Sasongko, Theopilus Bayu
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3243

Abstract

Menangis merupakan salah satu usaha bayi dalam berkomunikasi untuk menyampaikan suatu kondisi yang sedang dialaminya, baik itu sedang lelah, sakit perut, rasa tidak nyaman maupun lapar. Bagi sebagian orang tua yang baru memiliki anak tentu tidak selalu mampu untuk memahami apa yang dikehendaki oleh bayi ketika dia menangis, karena suara tangisan yang dihasilkan terdengar hampir sama. Maka, pada penelitian ini dibuat sebuah sistem klasifikasi makna tangisan bayi dengan mengimplementasikan deep learning. Untuk memahami arti tangisan bayi berdasarkan penyebabnya dengan mengimplementasikan metode Mel-Frequency Cepstral (MFCC) sebagai fitur ekstraksi ciri dan CNN sebagai metode klasifikasi. Diantara proses pelatihan dan pengujian yang telah berhasil dilakukan dalam penelitian ini diperoleh hasil akurasi tertinggi terhadap pelatihan yang dilakukan dengan 50 epoch sebesar 93,84% dan model mampu mengklasifikasikan makna tangisan bayi berdasarkan penyebabnya terhadap data baru dengan rata-rata akurasi 88.04%.
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.
Project-Based Learning (PjBL) Model in E-Module as an Improvement of Critical Thinking in the Department of Cosmetology And Beauty Rahmi Oktarina; Siska Miga Dewi
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3264

Abstract

The goal of this research is to enhance learning and critical thinking abilities of Cosmetology and Beauty students in e-commerce courses through the implementation of interactive e-commerce e modules. This study uses a quasi-experimental approach with control groups for the pretest and posttest. Data collection was carried out using instruments in the form of open questionnaires and tests. The study's data analysis method is quantitative descriptive analysis, differentiation power analysis using T Test and qualitative descriptive analysis. The independent sample t test findings describe a Sig (2 Tailed) greater in the experimental class compared to the control class, it can be inferred that there is a substantial difference between the two groups. The overall findings support the usage of the e-module in e-commerce courses because it has been shown to enhance critical thinking abilities and learning outcomes.
Klasifikasi Penyakit Bawang Merah Menggunakan Naïve Bayes dan Convolutional Neural Network Dian; Purnawansyah; Darwis, Herdianti; Nurhayati, Lilis
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3265

Abstract

Bawang merah rentan terhadap serangan penyakit yang dapat mengganggu pertumbuhan dan mengakibatkan hasil panen yang tidak maksimal bahkan gagal panen, seperti bercak ungu dan moler. Penelitian ini bertujuan untuk mengklasifikasikan penyakit bawang merah dengan mengimplementasikan meetode naïve bayes (gaussian , bernoulli, dan multinomial) dan CNN pada citra bawang merah yang diekstraksi menggunakan fourier descriptor. Metode FD – CNN memperoleh tingkat accuracy 98% dalam mengklasifikasikan penyakut bawang merah, moler dan bercak ungu, sedangkan metode CNN tanpa menggunakan ekstraksi menghasilkan nilai accuracy sebesar 97%. Adapun pada metode naïve bayes, pengklasifikasian yang memiliki accuracy paling tinggi adalah metode gaussian naïve bayes sebesar 95% sedangkan yang paling rendah yaitu metode bernoulli naïve bayes dengan tingkat accuracy sebesar 42%. Dengan demikian, dapat disimpulkan bahwa CNN, FD-CNN, dan FD-GNB efektif untuk meningkatkan performa klasifikasi pada citra daun bawang merah.
Integration of Artificial Intelligence in Education: Opportunities, Challenges, Threats and Obstacles. A Literature Review. Saputra, Indra; Astuti, Murni; Sayuti, Muhammad; Kusumastuti, Dyah
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3266

Abstract

The research background is the rapid development of AI which affects various aspects of education. The purpose of this study is to analyze in depth about the opportunities, challenges, threats and obstacles to the implementation of AI in education. The research method used in this study is semy-systematic literature review. The analysis technique used is a meta-narrative approach that includes the process of identifying, analyzing, recognizing patterns and topic-related themes. The results describe that AI opportunities in education are related to the delivery of learning materials, evaluation, management systems, and educational policy making. Meanwhile, the challenges are related to pedagogy, educational frameworks, and literacy. Threats that arise are related to the security of personal data, character building and educational ethics. Finally, obstacles that arise include the high costs required, limited teacher and professional training schemes in preparing AI competencies, and slow changes in curriculum structure and structural level of education.

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