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
 36   37   38   39   40 
Sistem Rekomendasi Produk dengan Metode ABC-Cycle Counting untuk Pelayanan dan Pengelolaan Inventory Toko : Store Service and Inventory Management Application Using Product Recommendation System with ABC-Cycle Counting Method Nurrido, Dodi; Triase
The Indonesian Journal of Computer Science Vol. 12 No. 6 (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.v12i6.3608

Abstract

Toko ashion adalah usaha yang biasanya menyediakan segala jenis fashion seperti baju, celana, sendal, dan aksesoris. Masih banyak terdapat toko fashion yang melakukan penanganan manajemen keuangan dan inventaris secara manual sehingga perkembangan usaha terhambat oleh perputaran kas yang kurang ideal karena disebabkan tidak adanya rencana perputaran pendapatan maupun pendataan produk yang dapat menimbulkan perputaran uang yang tidak maksimal dan menyebabkan usaha susah untuk berkembang. Oleh sebab itu, dibutuhkan sebuah sistem yang mampu membantu mengelola pendapatan maupun kegiatan usaha dengan membangunan Aplikasi Pelayanan dan Pengelolaan Inventory toko (Appfashion) berbasis website yang mampu merekomendasikan produk yang harus segera dibeli dengan menggunakan Metode ABC-Cycle Counting.
Lean and Agile Software Development for Managing Technical Debt on A Large-scale Software: A Systematic Literature Review Simangunsong, Surya Seven Y; Raharjo, Teguh; Anita Nur Fitriani
The Indonesian Journal of Computer Science Vol. 12 No. 6 (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.v12i6.3612

Abstract

Agile methodologies are employed by software development teams for collaboration and adapting to changing requirements. However, this flexibility may lead to technical debt (TD), causing potential bugs in the long term. Lean principles, focusing on waste elimination and continuous process improvement, can be applied to manage TD in agile software development. This research conducts a systematic literature review on using lean and agile methodologies for TD management. The review identifies 34 papers, categorizing TD types, pinpointing lean and agile principles, and aligning technical debt categories with suitable lean and agile principles. Additionally, three existing technical debt management frameworks are identified: the TAP framework, the LTD framework, and the CoDVA framework. The study concludes that integrating lean principles into agile software development assists organizations in effectively managing technical debt. Furthermore, the research offers insights into selecting the most suitable TD management framework based on an organization's needs and available resources.
Analisis Sentimen Putusan Mahkamah Konstitusi terhadap Batas Usia Capres dan Cawapres Menggunakan IndoBERT Septian, Luffi; Aljauza, Teguh; Juliane, Christina
The Indonesian Journal of Computer Science Vol. 12 No. 6 (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.v12i6.3614

Abstract

Putusan Mahkamah Konstitusi nomor 90/PUU-XXI/2023 tentang batas usia calon presiden dan wakil presiden telah memicu perbincangan masyarakat. Hal ini ditandai dengan kata kunci ‘Putusan MK” pada media sosial Twitter/X menduduki peringkat tiga trending topik Nasional selama pertengahan bulan Oktober. Putusan tersebut dinilai kontroversial karena berkaitan dengan momentum Pemilihan Presiden 2024. Peneliti tertarik untuk memanfaatkan data dari media sosial twitter/X dalam menganalisis respon masyarakat terhadap Putusan Mahkamah Konstitusi dengan cara mengklasifikasikan respon tersebut ke dalam sentimen. Model yang digunakan dalam penelitian ini adalah IndoBERT, sebuah arsitektur transformer BERT yang dikembangkan oleh tim IndoNLU. Metode ini dipilih berdasarkan efektivitasnya dalam memproses teks berbahasa Indonesia untuk mengidentifikasi dan mengategorikan opini publik menjadi positif, negatif, atau netral terkait dengan keputusan Mahkamah Konstitusi. Hasil awal menunjukkan model IndoBERT tanpa augmentasi data mencapai akurasi 0.81 dan F1 skor 0.58. Selanjutnya, penggunaan teknik Synthetic Minority Over-sampling Technique (SMOTE) meningkatkan F1 skor namun tidak berdampak signifikan pada akurasi. Eksperimen selanjutnya dengan augmentasi random swap, menghasilkan peningkatan performa yang substansial, dimana model IndoBERT mencapai akurasi dan F1 skor sama-sama pada angka 0.90.
Tren Riset Sistem Informasi Dalam Publikasi Internasional: Analisis Bibliometrik Menggunakan VosVIEWER Lizar, Yaslinda; Siregar, Elfi Ramadani; Rosmita, Ummi; Silvia Ariati; Masyaratul Hayati
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): 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.v13i5.3615

Abstract

Sistem informasi pada era ini sedang sangat hangat diperbincangkan dalam dunia IT. Tentunya hal ini akan berdampak pada peningkatan minat para peneliti untuk mempelajari serta mendalami pemahaman terkait sistem informasi dalam kurun waktu 5 tahun terakhir. Jurnal ini dibuat dengan maksud untuk memetakan tren penelitian sistem informasi melalui kajian bibliometrik terhadap 20.000 artikel jurnal internasional terindeks Scopus dalam jangka tahun 2019-2023. Analisis kejadian bersama kata kunci dengan VOSviewer mengidentifikasikan 4 kelompok utama dalam penelitian sistem informasi ini. Cluster dengan kepadatan tertinggi pada topik gis, patient, theory, dan outcome. Dan topik seperti magnesium, annual percent change, lightgbm, postoperative length tidak terlalu banyak diteliti. Pemetaan ini diharapkan dapat memberikan pengetahuan tentang arah penelitian sistem informasi di masa depan, terutama pada topik-topik yang masih sedikit dieksplorasi.
Machine Learning on Opinion Mining of Netizen's Hate Speech Pratiwi, Mutiana; Liana Gema, Rima
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): 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.v13i1.3617

Abstract

Netizen comments written in an online news portal through social media platforms, one of which is Instagram, can be used as material in the sentiment analysis process, which can be classified into positive, negative, or neutral sentiments. Sentiment analysis is part of the study of text mining, the science of discovering unknown knowledge by automatically extracting information from large volumes of unstructured text into useful information. The resulting information is in the form of sentiment towards a topic, whether it tends to be positive, negative, or neutral. The classification method used in this research is Support Vector Machine (SVM) and TF-IDF data weighting to classify text. Stages to perform data analysis are pre-processing to clean data, word weighting, labeling data into positive, negative, or neutral classes, and classifying and visualizing data with graphs. Accuracy tests using 70:30 split data showed that the accuracy reached 98%. Tests with 80:20 and 90:10 split data also showed high accuracy of 98% and 99%.
Algorithm Decision Tree in Analysis Social Media Sentiment to Understand Consumer Views of Brands Pramana Gusman, Aggy; Andrianof, Harkamsyah
The Indonesian Journal of Computer Science Vol. 12 No. 6 (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.v12i6.3619

Abstract

Online marketplaces in Indonesia are growing rapidly and have become one of the main destinations for internet and social media users. Various marketplace services are available and accessed by the majority of Indonesians. However, with this development, consumer satisfaction with marketplace services varies, from positive, to negative, to neutral. Many consumers express their reactions on social media, including Twitter. In this study, an analysis of opinions was conducted on posts by online business customers in Indonesia on Twitter from various consumers. However, due to the large number of comments, it is difficult to conclude the customer's opinion about online shopping sites that offer the best services. Even trending topics on Twitter only display hot topics that are widely discussed without clear conclusions. To classify general opinion data on Twitter from e-commerce sites, the first step is to process tweet data using Rapidminer tools to recognize the tweet data. Then, the decision tree algorithm is used to categorize opinion data. The results showed that using cross-validation, the decision tree algorithm achieved an accuracy of 70.27 percent while using split validation, it achieved an accuracy of 66.95 percent. In this case, better accuracy was achieved using cross-validation. The results of this study can provide useful information for online businesses in Indonesia to improve the quality of their services and increase customer satisfaction. In addition, this study also provides an overview of the importance of utilizing the decision tree algorithm in categorizing opinion data on social media, especially on Twitter, as a tool for analyzing consumer sentiment towards a service or product.
Komparasi Pendidikan Kewirausahaan Di Indonesia Dan China Melalui Kewirausahaan Teknologi Informasi Digital Oriza, Wike; Ganefri; Giatman; Yulastri, Asmar; Maksum, Hasan
The Indonesian Journal of Computer Science Vol. 12 No. 6 (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.v12i6.3620

Abstract

Tujuan dari penelitian ini untuk mengetahui faktor yang mempengaruhi minat berwirausaha di Indonesia dan China serta perbedaan yang dimiliki dan untuk mengetahui perbandingan atau komparasi pendidikan kewirausahaan antara Indonesia dan China. Metode yang digunakan dalam penelitian ini yaitu study literatur kemudian dianalisis dan ditarik kesmpulan. Hasil kajian literatur menunjukkan bahwa Pendekatan pendidikan kewirausahaan di kedua negara dapat disesuaikan dengan mempertimbangkan perbedaan yang ditemukan untuk memenuhi minat dan motivasi kewirausahaan mahasiswa masing-masing negara. Di China, fokus dapat diberikan pada membangun keyakinan mahasiswa untuk mengatasi rintangan, sementara di Indonesia, penekanan dapat diberikan pada memperkuat kepercayaan bahwa berwirausaha memberikan manfaat. Selain itu, penggunaan profil wirausaha sukses, terutama wirausaha muda, dapat menjadi motivator yang efektif di kedua negara. Indonesia maupun China telah mengambil langkah-langkah penting dalam mengembangkan pendidikan kewirausahaan. Meskipun keduanya menghadapi tantangan yang berbeda, baik Indonesia maupun China memiliki potensi besar untuk terus memajukan pendidikan kewirausahaan dan mendukung generasi muda dalam membangun bisnis yang inovatif dan berkelanjutan. Tumbuh dan berkembang pesatnya kewirausahaan di China dan diIndonesia tidak lepas dari peran teknologi informasi digital. Dunia digital menawarkan sumber daya baru yang sangat luas bagi wirausahawan untuk memanfaatkan peluang mempromosikan bisnis yang digelutinya. Mulai dari pengumpulan data terbuka, konten, kode dan layanan yang tumbuh secara eksponensial hingga kontribusi online penggunan dan komunitas di seluruh dunia. Keywords : Minat Berwirausaha di Indonesia dan China, Pendidikan Kewirausahaan di Indonesia dan China, Teknologi digital.
Optimalisasi Metode Naive Bayes Classifier Untuk Prediksi Persetujuan Kredit Syakur, Achmad; Purwandi Putra, Rendri; Juliane, Christina
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): 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.v13i1.3622

Abstract

Kredit adalah bentuk pembiayaan yang banyak orang ajukan ke bank atau perusahaan penyedia kredit. Dalam proses pengajuan kredit, dilakukan analisis untuk menentukan apakah kredit yang diajukan layak atau tidak. Penelitian ini bertujuan untuk membantu bank atau perusahaan penyedia kredit dalam melakukan persetujuan kredit dengan efektif dan akurat dalam menentukan status pengajuan. Penelitian ini menggunakan teknik data mining dan kumpulan dataset yang berasal dari kaggle.com. Terdapat 12 atribut dan 2 kelas yang digunakan dalam penelitian ini. Dalam penelitian ini, metode klasifikasi Naive Bayes dan optimasi kelompok partikel (PSO) digunakan. Prediksi persetujuan kredit dengan metode naïve bayes classifier menghasilkan nilai akurasi sebesar 80,00% dengan nilai AUC 0,884. Sebaliknya, prediksi persetujuan kredit dengan metode particle swarm optimization (PSO) menghasilkan nilai akurasi sebesar 96,67% dengan nilai AUC 0,69.
Cybersecurity Improvement Design in Critical Infrastructure PT XYZ Case Study Gunawan, Adi; Rizal Fathoni Aji
The Indonesian Journal of Computer Science Vol. 12 No. 6 (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.v12i6.3624

Abstract

Prevention of cyber attacks is an important factor that organizations need to do to support maximum business process continuity. One of the important factors needed is the protection of data and information assets by the critical infrastructure owned. This paper discusses increasing the capacity of critical infrastructure at XYZ organization. The NIST CSF framework is a benchmark assessment tool for critical infrastructure capacity at PT XYZ. The results obtained from the assessment are used to make recommendations for controls related to critical infrastructure. The risk assessment found 107 risk scenarios that led to 11 recommendations for critical infrastructure improvement areas. Prioritization of the resulting recommendations is expected to increase the resilience and capacity of critical infrastructure at PT XYZ in the face of current and future cyber threats.
Platform Budidaya Perairan Ekosistem Tambak Berbasis Internet Of Things Arisdiawan, Rossi; Setiawardhana; Agus Indra Gunawan
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): 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.v13i1.3627

Abstract

Indonesia has a potential pond area of 2,963,717 hectares. Ponds are a place for breeding ecosystems such as shrimp. The digitalization system in ponds is very necessary for management and development and has an impact on economic growth. Development in the field of aquaculture involves extensification and intensification. One of the intensification programs is to utilize Internet of Things (IoT) technology to identify various parameters from the Pond which are sent to the Webserver. This research is to produce a webserver-based platform to serve as a data center and monitor several IoT devices on the farm. This platform uses an internet network with HTTP and MySql protocols. Operations related to web servers and devices refer to standard quality settings from pond farmers.

Page 38 of 117 | Total Record : 1170

 36   37   38   39   40 

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