cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tri A. Sundara
Contact Email
tri.sundara@stmikindonesia.ac.id
Phone
+628116606456
Journal Mail Official
ijcs@stmikindonesia.ac.id
Editorial Address
Jalan Khatib Sulaiman Dalam 1, Padang, Indonesia
Location
Kota padang,
Sumatera barat
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 1,170 Documents
 19   20   21   22   23 
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
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
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
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
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
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
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.
Klasifikasi Tinggi Badan Menggunakan Metode Mask R-CNN Permana Sanusi, Amadea; Fariza, Arna; Setiawardhana
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.v12i4.3348

Abstract

Tinggi badan adalah parameter penting saat memasuki sebuah wahana. Penggunaan alat keselamatan saat bermain wahana permainan tidak akan maksimal jika wisatawan tidak memiliki tinggi badan yang sesuai dengan kriteria untuk memasuki wahana tersebut. Dalam penerapannya, seleksi wisatawan yang diperbolehkan masuk ke dalam wahana permainan masih menggunakan pengukuran tinggi badan secara manual. Penelitian ini bertujuan untuk mengurangi resiko terjadinya kecelakaan pada kendaraan dengan mengklasifikasikan dan mengimplementasikan sistem otomasi menggunakan pendekatan deep learning. Penggunaan deep learning yang berkembang saat ini dapat digunakan untuk mengklasifikasikan pengunjung. Penelitian ini mengusulkan proses klasifikasi tinggi badan menggunakan metode Mask R-CNN yang dapat digunakan untuk melakukan klasifikasi lebih dari satu orang, sehingga mempercepat antrean wisatawan pada wahana permainan. Hasil pengujian menunjukkan bahwa model Mask R-CNN yang dibangun berhasil mengklasifikasikan objek dengan memberikan bounding box, masking, dan label yang sesuai dengan objek. Membangun model Mask R-CNN sangat dipengaruhi oleh variatif gambar pada dataset dan proses anotasi gambar di dalam dataset. Evaluasi model menunjukkan hasil perhitungan mAP yang didapatkan sebesar 71%. Penelitian ini telah memenuhi tujuan utama dalam penelitian karena model Mask R-CNN berhasil melakukan klasifikasi yang sesuai.
Klasifikasi Teks Pada Ulasan Objek Wisata di Kota Pagar Alam Menggunakan Pendekatan Machine Learning Dewi, Nadiya Citra
The Indonesian Journal of Computer Science Vol. 12 No. 5 (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.v12i5.3349

Abstract

Tujuan dari penelitian ini adalah untuk menghasilkan Klasifikasi Teks Pada Ulasan Objek Wisata di Kota Pagar Alam Menggunakan Pendekatan Machine Learning. Latar belakang Penelitian ini yakni belum adanya pengolahan data teks pariwisata kota Pagar Alam Pada halaman Google Review sehingga belum ada klasifikasi teks ulasan positif dan negative terhadap wisata Kota Pagar Alam Penelitian ini akan menggunakan wisata gunung dempo kota Pagar Alam dengan Jumlah data yang didapatkan sebanyak 521 data dari metode web scrapping dengan Bahasa pemrograman python. Metode yang digunakan K-Nearest Neighbor (K-NN). Teks diolah Menggunakan Bahasa Pemrograman Python 3 dengan bantuan Google Colab untuk melakukan Cleansing, Tokenizing, Filtering, Stemming, case folding dan stop words hingga ke TF-IDF Adapun simpulan yang dapatkan dalam penelitian ini adalah frekuensi kemunculan text yang paling yakni text “keren” dan “indah” dengan nilai TF IDF 25,15 dan 24,47. Dadapatkan juga klasifikasi text ulasan positif sebanyak 455 dan ulasan negative sebanyak 66. Kemudian didapatkan akurasi tinggi yakni sebesar 87 % hal ini menunjukan bahwa klasifikasi text pada ulasan wisata gunung dempo kota Pagar Alam sangat baik dengan nilai akurasi 87 %.
Decision Support System for Determining the Best Online Learning Application Using the Weighted Product Method (WPM) Yeka Hendriyani; Mariani; Rahmatika, Hayati; Akbar Ilahi; Hardeyenti, Dessy
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.v12i4.3352

Abstract

This article discusses the use of the Weighted Product (WP) Method in a Decision Support System to determine the best online learning application. The goal is to help users choose a learning app that suits their needs and preferences. Relevant criteria, such as user interface, learning content, interactive features, difficulty level, and subscription price, are identified and assigned relative weights. This study uses data from various online learning apps to train the WP model and rank the apps based on the highest scores. The experimental results show that the WP method successfully identifies the best online learning apps with high accuracy, allowing users to have an effective and efficient learning experience according to their individual goals. Thus, this SDM can be an effective guide for users in making informed decisions to meet their education and learning needs
Performance Evaluation IT Governance on Universities: COBIT 2019 Approach with Measurement Capability Levels Marcela, Elisabet Dela; Melissa Indah Fianty
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.v12i4.3353

Abstract

The utilization of the Peoplesoft Campus Solution (MyUMN) technology, which has become excessively outdated, necessitates an evaluation, development, and rejuvenation of MyUMN to address any occurring issues with definitive solutions. An assessment of the Information Technology governance capability level is carried out using the COBIT 2019 framework. This research focuses on the following specific objectives: BAI03 (Managed Solutions Identification and Build), BAI06 (Managed IT Changes), and BAI07 (Managed IT Change Acceptance and Transitioning). The measurement results reveal that the capability levels for all three objectives are at level 2, while the targeted capability level is at level 4. Consequently, there exists a gap between these two levels. Recommendations encompass prioritizing the documentation of application development and conducting routine reviews of all completed change requests to ensure the alignment of change requests

Page 21 of 117 | Total Record : 1170

 19   20   21   22   23 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 4 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science More Issue