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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 1,106 Documents
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Analisis Faktor Perilaku Penggunaan Sistem Pendaftaran Online Berdasarkan Unified Theory of Accepptance and Use of Technology (UTAUT) Suradi, Asy Syfa; Firdawati; Pujani, Vera; Fiandra, Yudha
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.v12i3.3177

Abstract

Penelitian ini bertujuan mengevaluasi sikap penerimaan masyarakat terhadap penggunaan pendaftaran online di RS Aisyiyah Pariaman menggunakan model Unified Theory of Accepptance and Use of Technology (UTAUT). Penelitian ini merupakan penelitian observasional analitik dengan pendekatan cross sectional yang menjelaskan hubungan variabel independen (performance expectancy, effort expectancy, social influence dan facilitating conditions), variabel moderasi (usia dan pengalaman) terhadap variabel dependen (use behavior dan behavioral intention). Populasi adalah pasien poliklinik rawat jalan menggunakan pendaftaran online. Sampel yang diteliti sebesar 243 sampel dengan menggunakan teknik purposive sampling dalam pengambilan sampel. Metode analisis data deskriptif menggunakan SPSS 26 dan analisis Structural Equation Modelling (SEM) menggunakan piranti LISREL 8.8. Berdasarkan hasil penelitian didapatkan bahwa performance expectancy, effort expectancy, social influence berhubungan signifikan terhadap behavior intention dan facilitating conditions, behaviaoral Intention berhubungan signifikan terhadap use behavior, sedangkan variabel moderasi usia dan pengalaman tidak berhubungan signifikan dalam memoderasi variabel independen terhadap variabel dependen.
Prediksi Diskon Harga Fashion Pria Pada Ecommerce Menggunakan Jaringan Syaraf Tiruan Backpropagation Siregar, Andree Rizky Yuliansyah
The Indonesian Journal of Computer Science Vol. 12 No. 2 (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.v12i2.3179

Abstract

Pada saat ini teknologi bergerak sangat cepat, sehingga manusia tidak peduli dengan batas, jarak, ruang dan waktu. Masyarakat kini sering menggunakan smartphone untuk berbelanja online dengan internet sebagai penunjang penggunaan smartphone yang juga menjadi sumber informasi bagi konsumen. Masyarakat dapat berbelanja online melalui toko ecommerce. Shopee merupakan salah satu ecommerce yang sering menawarkan diskon pada dua tanggal, bulan dan pada waktu tertentu untuk menarik konsumen. Namun, masyarakat sering tidak mendapatkan produk yang mereka inginkan saat diskon tersedia. Maka dari itu dengan adanya penelitian ini untuk mengetahui harga diskon pakaian pria di ecommerce shopee pada tanggal, bulan dan waktu yang diberikan. Website ini dapat membantu konsumen dalam mendapatkan barang diskon pada tanggal, bulan dan waktu yang ditentukan oleh shopee. Hal ini dapat meningkat akurasi sebanyak 89% dengan produk kaos lengan panjang pada tanggal 11 dan bulan 11.
Comparison of Machine Learning Algorithm Models in Bitcoin Price Sentiment Analysis Afrinanda, Rizky; Tawa Bagus, Wahyu; Efrizoni, Lusiana
The Indonesian Journal of Computer Science Vol. 12 No. 2 (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.v12i2.3180

Abstract

Bitcoin is one of the digital payments that is currently booming, fast delivery makes bitcoin in great demand by many people, currently there are many digital currency exchanges that can be used, one of the well-known ones in Indonesia, namely Indodax. Indodax is a cryptocurrency exchange, not only an exchange, Indodax also provides a chat room containing investors' opinions. Opinions contained in the Indodax chat room can be used to determine whether comments are positive, neutral or negative, so that it can be an investor's decision to sell or buy bitcoin using sentiment analysis. The sentiment analysis process begins with collecting data using an instant data scraper on the Indodax website, data preprcoessing, labeling using vader lexicon, TF-IDF as word weighting, data splitting, naïve Bayes algorithm and support vector machine, feature selection xgboost and gradient boosting, model evaluation with confusion matrix, then comparing the results of the two algorithms. Based on the tests that have been carried out, naïve bayes obtained the best accuracy value of 70.7%, naïve bayes combined with XGBoost obtained the best accuracy value of 86.6%, while the Support vector machine obtained the best accuracy 86.1%, support vector machine combined with gradient boosting obtained the best accuracy value of 88%. Based on these results the use of feature selection can increase the accuracy value of the algorithm.
Factors Influencing Students’ Continuance Intention in Learning through MOOCs: A Systematic Literature Review Romadhon, Muh Syaiful; Junus, Kasiyah; Santoso, Harry B.; Ahmad, Mubarik; Purwandari, Endina Putri
The Indonesian Journal of Computer Science Vol. 12 No. 2 (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.v12i2.3181

Abstract

Massive Open Online Courses or MOOCs advocate the "democratization of education”, which makes education available for everyone anywhere and anytime. The number of students who registered for a MOOC demonstrates that their intention to use MOOCs is reasonably high, yet only 7-10% complete the course. This review conducts literature review on frameworks or theories, instruments, and major factors that influence the intention to persist in MOOCs. A total of 150 articles spanning the years 2018–2022 are initially reviewed guided by PRISMA framework, from which 20 are selected based on the selection criteria in this study. Self-developed model and TAM has become the most often used theory to determine a persons’ continuance intention on MOOCs. The majority of studies utilized SEM and PLS-SEM as instruments to analyse the continuance intention data. Perceived usefulness is the most important and influential factor in MOOCs.
Comparative Analysis of Open Source Security Information & Event Management Systems (SIEMs) Bezas, Konstantinos; Filippidou, Foteini
The Indonesian Journal of Computer Science Vol. 12 No. 2 (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.v12i2.3182

Abstract

A Security Information and Event Management system (SIEM) is a tool used to collect, analyze, normalize and correlate data from various devices to identify potential cyber threats almost in real-time. SIEM provides a unified approach to security issues through two zones: Security Information Management (SIM) and Security Event Management (SEM). SIM deals with managing logs and reporting, while SEM deals with event management and real-time monitoring. SIEM tools collect data events in a central unit from various devices, normalize their format, analyze them, and generate reports and alerts. SIEM combines the ability of log management to generate a compliance report with the ability to manage threats. However, the central approach may present significant disadvantages, such as slowing system performance and complicating the prioritization of queries.
A Deep Learning Model for Answering Why-Questions in Arabic Azmi, Aqil; Alwaneen, Tahani
The Indonesian Journal of Computer Science Vol. 12 No. 2 (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.v12i2.3183

Abstract

The subfield of natural language processing (NLP) known as question answering (QA) involves providing answers to questions posed in natural language. Answering “why” questions has long been a challenging task for QA systems, given the complexity of the reasoning involved. In this paper, we propose a deep learning model for answering “why” questions in Arabic. Recent advances in neural network models have yielded promising results across a range of tasks, particularly with the integration of attention and memory mechanisms. Our proposed model is based on the dynamic memory network (DMN), an architecture that utilizes attention and memory mechanisms to locate and extract relevant information for answering a question. We evaluate the performance of our DMN-based model in answering Arabic “why” questions using the LEMAZA dataset, achieving an F-score of 78.61%. Our findings suggest that DMN-based models hold promise for addressing the challenge of answering “why” questions in Arabic and other languages.
Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Program BPJS Ketenagakerjaan Meiriza, Adellia; Ali, Edwar; Rahmiati; Agustin
The Indonesian Journal of Computer Science Vol. 12 No. 2 (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.v12i2.3184

Abstract

BPJS Ketenagakerjaan bertugas menyelenggarakan program jaminan sosial bagi para pekerja di Indonesia, seperti Jaminan Kecelakaan Kerja, Jaminan Hari Tua, Jaminan Pensiun, Jaminan Kematian, dan Jaminan Pemeliharaan Kesehatan. Pengelompokan program bukan penerima upah dapat menggunakan metode clustering. Dalam penelitian ini, peneliti membandingkan dua algoritma clustering yaitu K-Means dan K-Medoids untuk mengelompokkan program bukan penerima upah berdasarkan karakteristik yang dimiliki. Data yang digunakan dalam penelitian ini diperoleh dari BPJS Ketenagakerjaan cabang pekanbaru. Pengelompokan dilakukan dengan menggunakan jumlah cluster yang sama untuk kedua algoritma yaitu K = 3. Hasil dari penelitian menunjukkan bahwa K-Medoids menghasilkan kelompok yang lebih stabil dan robust dibandingkan dengan K-Means. Hasil nilai DBI menunjukkan bahwa K-Medoid lebih baik dari K-Means. Hasil ini dapat dijadikan rekomendasi kepada pendaftar yang akan mengambil program BPJS Ketenagakerjaan selain itu penggunaan K-Medoids sebagai algoritma clustering lebih efektif dibandingkan K-Means untuk pengelompokan program bukan penerima upah.
Opinion Mining menggunakan Algoritma Deep Learning untuk Menganalisis Penggunaan Aplikasi Jamsostek Mobile Azhari, Zahra; Efrizoni, Lusiana; Agustin, Wirta; Yanti, Rini
The Indonesian Journal of Computer Science Vol. 12 No. 2 (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.v12i2.3185

Abstract

BPJS Ketenagakerjaan berperan dalam menjaga kesejahteraan para pekerja dan buruh melalui program-program pendidikan dan pelatihan yang diberikan, pelayanan menjadi prioritas terhadap pelanggan untuk memberikan kenyamanan. Melalui aplikasi Jamsostek Mobile yang terdapat di google playstore akan diambil komentar-komentar untuk mendapatkan respon pelanggan terhadap aplikasi Jamsostek mobile untuk dilakukan opinion mining. Komentar yang diambil dari google playstore menggunakan bantuan googleplayscraper, sebanyak 3000 komentar berhasil diambil yang kemudian akan dilakukan tahap pembersihan data, pelabelan, pembobotan kata menggunakan word2vec 300 dimensi dan dilanjutkan menggunakan algoritma Long Short Term Memory. Hasil opinion mining menunjukkan dominasi sentimen negatif sebesar 80.58% dan 19.42% positif dengan tingkat akurasi terbaik yang dihasilkan oleh algoritma LSTM sebesar 87.36%. Hasil penelitian ini akan memberikan wawasan yang berguna bagi pengembang aplikasi untuk meningkatkan kualitas pelayanan dan pengalaman pengguna.
Design and Build Vocational Choice Applications in Vocational Schools Using the SAW (Simple Additive Weighting) Method Ardiyanti, Sri; Anwar, Muhammad; Hendriyani, Yeka; Waskito
The Indonesian Journal of Computer Science Vol. 12 No. 2 (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.v12i2.3186

Abstract

Vocational High Schools provide knowledge of skills, interests, and talents according to vocational fields to face the world of work. The phenomenon that occurred in February 2022 stated that most SMK graduates were ready to work. Because of this, the data from the Central Bureau of Statistics on SMK graduates have a total score of 10.38%. So there is unemployment and a lack of interest and talent in choosing a vocational field. Based on information in determining vocational fields based on interests, talents, and colleagues there is no discrepancy between the major one chooses and personality. In measuring products, the Simple Additive Weighting method can be used to select the best alternative in the ranking process which totals the weight values ​​of all criteria.
Hubungan Interaksi Belajar, Kemampuan Berpikir Tingkat Tinggi, dan Penguasaan Teknologi Informasi dan Komunikasi dengan Hasil Belajar Hidrolika Nadawina, Nadia; Rizal, Fahmi; Syah, Nurhasan; Ambiyar
The Indonesian Journal of Computer Science Vol. 12 No. 2 (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.v12i2.3187

Abstract

Tujuan dari penelitian ini adalah untuk mengetahui hubungan antara Interaksi Belajar, Kemampuan Berpikir Tingkat Tinggi, dan Penguasaan Teknologi Informasi dan Komunikasi dengan Hasil Belajar Hidrolika Mahasiswa Departemen Teknik Sipil FT UNP. Penelitian ini menggunakan pendekatan kuantitatif dengan mengumpulkan data melalui angket kuesioner, tes soal, dan hasil belajar mahasiswa. Analisis data dilakukan dengan menggunakan teknik regresi linear. Hasil penelitian menunjukkan bahwa Interaksi Belajar, Kemampuan Berpikir Tingkat Tinggi, dan Penguasaan Teknologi Informasi dan Komunikasi memiliki hubungan yang signifikan dengan Hasil Belajar Hidrolika mahasiswa. Implikasi dari penelitian ini adalah pentingnya meningkatkan Interaksi Belajar, Kemampuan Berpikir Tingkat Tinggi, dan Penguasaan Teknologi Informasi dan Komunikasi pada mahasiswa untuk meningkatkan hasil belajar di bidang Hidrolika dan disiplin ilmu lainnya.

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