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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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An Approach for Early Heart Attack Prediction Systems Using K-Means Clustering and Cosine Similarity Novita, Nanda; Saleh, Amir; Azmi, Fadhillah
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.v12i4.3324

Abstract

In this study, we used cosine similarity and k-means clustering to construct a system to predict heart attacks. In order to divide patient data into groups with distinct clinical profiles based on their clinical characteristics, the k-means clustering approach is used. The new patient profiles were also contrasted with predetermined risk group profiles using the cosine similarity method. Heart attack high-risk patients are those with a profile that resembles that of the high-risk category. This suggested prediction system offers numerous benefits and contributions. First, the technique helps identify individuals who are at high risk of having a heart attack, allowing for prompt intervention and treatment. Second, the technology aids in lowering the mortality and effects of a heart attack by foreseeing the possibility of one in high-risk patients. Combining the k-means clustering method and cosine similarity, this system can predict heart attacks with an accuracy and dependability of 93.71%. In order to aid medical practitioners in making wise decisions and enhancing patient care, this research offers fresh perspectives on how to understand and manage heart attacks.
Development of a Local Area Network with Netsupport in Learning in a Computer Laboratory Putra, La Ode Alwin Syahputra; Effendi, Hansi; Hendriyani, Yeka; Ambiyar
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.v12i4.3327

Abstract

Technological developments are increasingly advanced so as to increase the use of computer laboratories in higher education, especially in the learning process. However, this is not matched by the completeness of adequate infrastructure, which causes problems when using the laboratory. This study aims to develop and update a local area network with net support for learning in computer labs 5 and 6. This research uses the network development life cycle (NDLC) method. Data collected by questionnaire technique. The results of this study indicate that the validator test assessment from the innovation aspect of the IT infrastructure component is 89.17% so that the level of validity can be interpreted as valid in use, from the information system aspect it is 93.55%, so it can be interpreted as valid in use, practicality (group A) practicality test of 92.60%, so that it can be interpreted as practical to use, practicality (group B) practicality test is 94%, so the level of practicality can be interpreted as practical to use, practicality (Lecturer) practicality test is 95.23%, so that the practicality level can be interpreted as practical used, the effectiveness (group A) of the effectiveness test was 94.55%, so that the level of effectiveness could be interpreted as effectively used, the effectiveness (group B) of the effectiveness test was 93.70%, so the level of effectiveness could be interpreted as effectively used, effectiveness (labor assistant ) effectiveness test of 93.58%, so that the level of effectiveness can be interpreted as effectively used. So that it can be concluded that the development of a local area network network with netsupport in computer laboratory learning is feasible to use.
Enhancing Gaming Engagement through the Integration of Game Design Document and Finite State Machine: A Study on Optimizing Non-playable Character Responsiveness Wiratama, Jansen; Rian; Evelin Johan, Monika; Santoso, Hari
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.v12i4.3328

Abstract

Non-playable Character (NPC) is a prominent terminology within the gaming domain. While players do not directly control NPCs, their role significantly influences the gaming experience. Enhancing NPC responsiveness becomes imperative as NPCs' passive nature can lead to monotonous gameplay. To address this concern, a computational model, namely Finite State Machine (FSM), is implemented to elevate NPC responsiveness during interactions with the main characters, whether as adversaries or allies. This research uses the Game Design Document (GDD) methodology to design a survival horror-themed game. The resultant Survival Horror Game undergoes Alpha testing to validate its overall functionality and the successful integration of the FSM computational model. Findings indicate that enemy NPCs can pursue the main character from any position, with an average arrival interval of 68 seconds. Additionally, allied NPCs promptly respond when the main character approaches. Subsequently, the Beta testing results reflect an 80% average score interpretation based on percentage responses, implying the game's favorable suitability and acceptability on the Likert scale.
Analisis Random Forest Menggunakan Principal Component Analysis Pada Data Berdimensi Tinggi Diba, Farah; Lydia, Maya Silvi; Sihombing, Poltak
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.v12i4.3329

Abstract

Data yang memiliki dimensi tinggi membutuhkan metode machine learning yang mampu bekerja lebih cepat dan efektif dalam proses klasifikasi. Salah satu algoritma yang mampu menangani data kompleks adalah Random Forest. Random Forest bekerja dengan membangun beberapa decision tree secara random sebagai acuan feature selection. Namun, data berdimensi tinggi membutuhkan ruang penyimpanan yang lebih besar sehingga mengakibatkan lamanya proses komputasi. Oleh karena itu, Principal Component Analysis merupakan salah satu metode reduksi dimensi dalam merepresentasikan data berdimensi tinggi. PCA akan membentuk beberapa Principal Component yang mengandung informasi penting dari data asli. Dataset yang digunakan pada penelitian ini bersumber dari kaggle repository terdiri atas 26 atribut dan 129880 intances. Hasil dari penelitian ini RF dengan dengan n_estimators = 7 setelah direduksi PCA memiliki akurasi terbaik yaitu 90,13% pada data water quality.. Hal ini membuktikan bahwa PCA mampu mereduksi dimensi dengan membentuk pohon n_estimators sebanyak 7.
A Comparative Study of Computer Programming Challenges of Computing and Non-Computing First-Year Students Mbiada, Alain; Isong, Bassey; Lugayizi, Francis
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.v12i4.3330

Abstract

The learning of computer programming comes with unique difficulties that vary among students depending on their backgrounds, learning methods, and objectives. This paper investigates the programming challenges first-year students from non-computing at the North-West University, South Africa, and computing backgrounds at the University of Dschang, Cameroon face. A questionnaire-based data collection method is utilized and categorizes participants based on their gender, age, fields of study, prior experiences in mathematics, statistics, English, and programming languages, lab use/access, learning strategies, and material preferences. The aim is to identify and analyze the student's understanding of the basic programming concepts and the specific challenges met during introductory programming modules. Analysis of the collected data shows that while a considerable percentage of non-computing students have prior experience in mathematics and English, they lack familiarity with programming. Equally, while most computing students are proficient in spoken English, they face significant challenges in programming, mathematics, and written English. Notable difficulties are experienced in grasping concepts like recursion, arrays, error handling, and function/procedure methods. Moreover, a comparative study reveals that both groups of students encounter similar challenges, however, non-computing students’ difficulties are more than their computing counterparts. This paper, therefore, suggests designing teaching methods and learning materials to specifically meet the needs of non-computer science students, and enhance their understanding and proficiency in computer programming.
Perbandingan Performa Algoritma Decision Tree untuk Klasifikasi Penerima Beasiswa Bank Indonesia Dimas Bayu Stiawan; Nugroho, Yusuf Sulistyo
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.v12i4.3339

Abstract

Beasiswa dapat diartikan bantuan yang bisa digunakan sebagai biaya penunjang pendidikan, diberikan oleh institusi/lembaga. Pelaksanaan seleksi mahasiswa yang berhak menerima beasiswa didasarkan pada syarat/ketentuan lembaga pemberi beasiswa. Klasifikasi kandidat yang dilakukan dengan Excel menyebabkan sasaran penerima beasiswa kurang tepat karena tidak konsisten disebabkan unsur subjektivitas. Penelitian ini bertujuan menggunakan algoritma Decision Tree ID3, C4.5, serta CART dalam melakukan perbandingan Penerimaan Beasiswa BI untuk melihat algoritma yang memiliki performa terbaik. Penelitian ini menggunakan data sebanyak 398 pendaftar Beasiswa Bank Indonesia tahun 2022 yang diperoleh dari Biro Kemahasiswaan UMS. Hasil evaluasi kinerja ketiga algoritma menunjukkan CART mencapai hasil tertinggi dalam accuracy, precision, dan recall, masing-masing 72%, 92,59%, dan 74,62%. Selanjutnya C4.5 memiliki nilai accuracy, precision, dan recall, masing-masing sebesar 69,33%, 92,30%, dan 71,64%. ID3 memiliki performa terendah, dengan accuracy, precision, dan recall berturutan 68%, 92,15%, dan 70,14%.
Metode SVM dan Naive Bayes untuk Analisis Sentimen ChatGPT di Twitter Atmajaya, Dedy; Febrianti, Annisa; Darwis, Herdianti
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.v12i4.3341

Abstract

Pertumbuhan pesat platform media sosial telah memberikan jalur baru bagi individu untuk mengungkapkan pendapat dan sentimen mereka. Analisis sentimen dari konten yang dibuat oleh pengguna di platform seperti Twitter menjadi semakin penting dalam memahami opini publik dan tren sosial. Penelitian ini bertujuan untuk membandingkan kinerja dua algoritma machine learning, Support Vector Machine (SVM) dan Naive Bayes, dalam menganalisis sentimen pengguna Twitter mengenai ChatGPT, sebuah model bahasa canggih. Sentimen akan diberi label menggunakan dua alat analisis sentimen yang terkenal, Vader dan Roberta. Penelitian ini menggunakan data Twitter sebanyak 1000 dataset yang terkait dengan ChatGPT dan mengevaluasi akurasi, presisi, dan recall dari model SVM dan Naive Bayes. Hasil penelitian ini menunjukkan perbedaan yang jelas dalam kinerja model: SVM yang digabungkan dengan Vader mencapai tingkat akurasi, presisi, dan recall sebesar 59%, dengan F1-score sebesar 55%. Secara signifikan lebih unggul dibandingkan dengan model sebaliknya, dimana SVM dengan label RoBERTa menghasilkan akurasi sebesar 55%, presisi sebesar 58%, recall sebesar 55%, dan F1-score sebesar 52%. Naive Bayes menunjukkan kinerja yang relatif lebih rendah. Dengan menggunakan Vader, Naïve Bayes mencapai tingkat akurasi dan recall sebesar 47%, presisi sebesar 46%, dan F1-score yang lebih rendah sebesar 32%. Sedangkan, menggunakan RoBERTa dengan Naive Bayes menunjukkan penurunan akurasi menjadi 43%, recall sebesar 43%, presisi sebesar 18%, dan F1-score sebesar 26%. Pengendalian SVM dinilai memiliki kinerja yang lebih unggul dalam mengolah analisis sentimen pengguna Twitter mengenai opini tentang ChatGPT.
Peramalan Kebutuhan Obat Menggunakan XGBoost Studi Kasus pada Rumah Sakit XYZ: Forecasting Drug Needs Using XGBoost: Case Study at XYZ Hospital Muhammad Dzul Asmi Alhamdi; Herman; Wistiani Astuti
The Indonesian Journal of Computer Science Vol. 12 No. 5 (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.v12i5.3344

Abstract

Obat memiliki peran yang penting dalam pelayanan farmasi di rumah sakit, dari menyelamatkan nyawa hingga menyembuhkan pasien, namun perencanaan obat masih dilakukan secara manual menggunakan metode manual sehingga menghambat proses perencanaan obat, penelitian ini menggunakan XGBoost untuk melakukan peramalan time series pada penggunaan obat. Data yang digunakan adalah data perbulan penggunaan obat pada kategori vital dan essential dari tahun 2017 hingga 2022, penelitian ini menggunakan data cuaca sebagai fitur eksternal untuk membantu model bekerja. Hasil penelitian menunjukkan model XGBoost memiliki skor rata-rata skor RMSE dan MAE yang lebih rendah pada obat vital dibanding ketika melatih obat essential, sehingga model yang dilatih masih perlu perbaikan dalam menggunakan model XGBoost untuk meningkatkan performa model.
Development of Short Tutorial Video Learning Media Instagram in the Motorcycle Chassis Maintenance Courses at Vocational High Schools Waruwu, Buala Jefriana; Jalinus, Nizwardi; Wulansari, Rizky Ema; Purwanto, Wawan
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.v12i4.3345

Abstract

This study uses a Research and Development (R&D) design, with the ASSURE development model. ASSURE development procedures, namely Analyze learner characteristics, State Standards and Objectives, Select Strategies, Technology, Media, and Materials, utilize media and materials, require learner participation, evaluate and revise. The data analysis technique used describes the validity, practicality, and effectiveness of the developed media. The product results obtained from this development research are video tutorial learning media. Based on the results of this study it was concluded that the developed media was declared "valid" by media experts with an average value of 0.77, and also declared "valid" by material experts with an average value of 0.85. The media developed was declared "practical" with a practicality value from the teacher's response with a result of 96.41% and a student response of 88.10%, and the video tutorials produced were effectively used with the results of a comparison of pretest and posttest learning outcomes seen from students' classical completeness of 100% and a gain score of 0.61 in the medium category. From the results of the research conducted, it can be concluded that short video tutorial learning media via Instagram are valid, practical and effective in the subject of maintaining motorcycle chassis in vocational high schools.
Comparative Analysis Between Naïve Bayes Algorithm and Decision Tree Loss Rate from Fire Disaster Data in DKI Jakarta Province Cuatanto, Ricardo; Sutomo, Rudi
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.v12i4.3347

Abstract

In urban locations like DKI Jakarta Province, fire poses a severe concern. Understanding the trends and variables that affect fire risk requires analysis of fire incidence data. To assess fire data in the DKI Jakarta Province, the method uses the Decision Tree and Nave Bayes algorithms. The Decision Tree identifies the primary causes of fires, whereas Naive Bayes forecasts fire risk using weather and historical data. These two algorithms' combined outputs offer a thorough understanding of the features and causes of a fire. By educating authorities and the public on how to manage this risk, this research helps to improve fire mitigation techniques. The safety and readiness for fire disasters in this area should increase. The accuracy of the two predictions made by the Naive Bayes algorithm is 75%. In contrast, the accuracy of the Decision Tree algorithm is 78%, leading to the conclusion that the Decision Tree approach is more helpful in categorizing the severity of fire disaster losses.

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