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Tri A. Sundara
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tri.sundara@stmikindonesia.ac.id
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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 1,170 Documents
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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.
Pengembangan Media Pembelajaran Komik Strip Berbasis Flipbook Digital Pada Mata Pelajaran Projek Ilmu Pengetahuan Alam Dan Sosial Elfina, Eni; Waskito; Darmi, Resmi; Maksum, Hasan
The Indonesian Journal of Computer Science Vol. 12 No. 4 (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.v12i4.3258

Abstract

Kurangnya media pembelajaran mengakibatkan peserta didik kurang aktif dan antusias ketika proses pembelajaran berlangsung. Diperlukan adanya media pembelajaran yang dapat membantu dan memfasilitasi dalam membangkitkan minat belajar dan meningkatkan hasil belajar peserta didik di SMK. Penelitian ini bertujuan untuk mengetahui proses perancangan media pembelajaran komik strip berbasis flipbook, mengetahui tingkat kelayakan media. Model penelitian pengembangan yang digunakan yaitu model Instructional Development Institute (IDI) yang meliputi tahap define, develop dan evaluate. Media pembelajaran komik strip berbasis flipbook dirancang menggunakan software dan hardware. Hasil penelitian diperoleh dari produk komik strip berbasis flipbook pada materi projek IPAS bagi kelas X SMK. Berdasarkan hasil tersebut dapat disimpulkan bahwa media pembelajaran Berbasis flipbook pada materi projek IPAS bagi peserta didik Kelas X SMK dapat digunakan dalam pembelajaran projek IPAS, karena bisa meningkatkan motivasi, minat belajar serta bisa dilakukannya pembelajaran mandiri bagi siswa yang ingin mengulang pembelajaran di rumah.
Implementation of Secret Key Generation on Mobile Crowdsensing Application to Secure Tracking Location of Motorcyclists Dewi, Ni Made Lintang Asvini; Sudarsono, Amang; Yuliana, Mike
The Indonesian Journal of Computer Science Vol. 12 No. 4 (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.v12i4.3259

Abstract

Mobile crowdsensing is a method for collecting many data from sensors on smartphone. In this research, mobile crowdsensing application will be developed to display location of motorcyclits who connected in an Ad-Hoc network along with a security system using the Secret Key Generation (SKG) scheme to generate a secret key that will be used to encrypt and decrypt the data. From the results it can be concluded that the highest measurement correlation is 0.0398 and the lowest is 0.0018 but after randomness extraction proccess, the highest correlation is 0.996 and the lowest is 0.978. After encryption, information of Alice and Bob is stored as random character in database and decrypted as plaintext as shown as in application. In the attacking result, the data after encrypted just shown random character in traffic monitor. When the eavesdroppers manipulate its IP address like Alice's, they can’t connect to Bob.
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
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
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
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.
Optimasi K-Nearest Neighbor dengan Grid Search CV pada Prediksi Kanker Paru-Paru Kusuma, Satya Tegar; Sasongko, Theopilus Bayu
The Indonesian Journal of Computer Science Vol. 12 No. 4 (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.v12i4.3267

Abstract

Kanker paru-paru adalah salah satu kanker paling mematikan di seluruh. Salah satu penyebab kematian pada penderita kanker paru-paru adalah tidak ada sistem untuk memprediksi kanker paru-paru secara optimal apakah pasien menderita kanker paru-paru atau tidak. Oleh karena itu, penelitian ini bertujuan untuk melakukan optimasi nilai K pada algoritma k-nearest neighbor (KNN) menggunakan metode grid search cv. Algoritma KNN dipilih karena pada berbagai penelitian memiliki tingkat akurasi yang lebih baik dibandingkan dengan algoritma supervised learning lainnya. Data yang digunakan pada penelitian ini bersumber dari data publik yang ada di kaggle. Berdasarkan penelitian dan pembahasan mengenai optimasi nilai K pada algoritma KNN menggunakan metode grid search cv didapatkan nilai K paling optimal yaitu 3 dengan tingkat akurasi 96%. Oleh karena itu, nilai K=3 sangat baik diterapkan pada algoritma KNN untuk memprediksi kanker paru-paru karena memiliki akurasi yang tinggi.

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