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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 1,170 Documents
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Fake News Detection Using Machine Learning Mohammed Amin, Pshko
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.v12i4.3310

Abstract

Today one of the fattest environments for opinion exchanging is the internet. Individuals can share their data or post any news they want through social media platforms. these data sharing platforms do not verify the users or the data and information they post. so, some users can share fake or untrusted data through these platforms on the Internet. Fake news is described as a propaganda tool against any Individual, society, government, or political party. Humans cannot detect and understand all the fake news or data through these online platforms. In this study, the challenge has been defined through a Machine learning concept. machine learning classification was applied to the dataset. Finally, a comparison of the working of these classifiers is presented along with the results, Decision Tree and Support Vector Machine classification models are performing well.
Development of Print Modules into Electronic Modules in Food Microbiology Courses Rosel, Ruhul Fitri Rosel; Faridah, Anni; Kasmita; Ambiyar
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.v12i4.3318

Abstract

Food microbiology is one of the compulsory and important subjects as a basis for advanced courses such asFood Control, Food Preservation and Food Technology. Students experience difficulties in understanding learning material because there is no valid and practical media to use. To overcome this problem, media development was carried out in the form of E-modules for Food Microbiology Courses. The aim of this research is to develop E-module on Food Microbiology course in terms of material, format and presentation. This research uses the methodResearch and Development. E-module development using 4D models. Number of test validatorsvalidity 6 lecturers, consisting of 3 media test validator lecturers and 3 material test validator lecturersas well as 27 students majoring in Culinary Management. The average result of the assessment of material validation is 0.84 with the Valid category. Data were analyzed using the Aiken's V formula. The average result of the E-module validation assessment was 0.85 in the Valid category. The overall average result of the E-module validation on Food Microbiology Course is 0.84 with the Valid category. The average percentage of practicality by lecturers is 86.3% in the very practical category while the average practicality by students is 83.29% in the very practical category. Based on the results of all analyzes canconcluded E-module Food Microbiology Course is suitable for use in learning.
Analisis Sentimen Ulasan Google Maps Kuliner di Bojonegoro Menggunakan Metode Naïve Bayes Fauziyah, Dewi Nur; Sanjaya, Ucta Pradema; Anggraini, Fetrika
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.v12i4.3319

Abstract

Sentiment analisis merupakan proses menganalisa yang berhubungan dengan sebuah konten atau produk. Analisa terserbut berdasarkan hal hal yang di rasakan oleh seseorang secara subyektif dalam merasakannya. Pada umumnya sentiment analisis ini di tulis oleh penguna dunia maya yang digunakan untuk informasi berdasarkan ulasan atau postingan. Text mining dan Natural language Processing sebuah bidang yang mempunyai irisan yang sama dalam bidang kecerdasan buatan. Ini sangat membantu seseorang dalam mencari nilai sebuah ulasan yang ditulis oleh seseorang memiliki nilai positif atau negative bahkan bisa di nilai netral. Dalam penelitian ini meneliti ulasan usaha kuliner di bojonegoro berdasaran data yang ada di google maps. Untuk metode klasifikasi mengunakan naïve bayes yang pada dasarnya mengunakan penilaian peluang pada setiap atributnya dan di evaluasi dengan confusion matrix. Hasil yang didapatkan dari penelitian ini mendapatkan nilai akurasi 90,28% recall 90,28% dan presisi 89.89%
Support Vector Machine untuk Analisis Sentimen Masyarakat Terhadap Penggunaan Antibiotik di Indonesia Darwis, Herdianti; Wanaspati, Nugraha; Anraeni, Siska
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.v12i4.3320

Abstract

Peningkatan penggunaan antibiotik secara global termasuk di Indonesia, seringkali irasional dan tanpa resep berpotensi menyebabkan resistensi bakteri. Analisis sentimen data Twitter menggunakan query "antibiotik" dapat membantu mengungkap opini publik. Penelitian ini bertujuan untuk menerapkan algoritma Support Vector Machine (SVM) dengan kernel linear, RBF, dan polynomial, menggabungkan berbagai metode seperti pelabelan dengan RoBERTa, pelatihan dengan 5 cross validation, dan tokenizing bigram. Tiga skenario digunakan dalam penelitian ini dan yang menghasilkan nilai akurasi tertinggi yaitu skenario ketiga yang menggunakan slangword dari ramaprakoso dan stopword dari sastrawi sebagai refrensi library untuk filtering, nilai setiap kernel: akurasi 99,88%, presisi 99,88%, recall 99,88%, dan f1 score 99,88%. Metode SMOTE juga mempengaruhi hasil ini. Dari hasil pengujian, dapat disimpulkan bahwa SVM efektif untuk analisis sentimen.
Development of Android-Based Learning Media in Information Communication Technology (ICT) at Senior High School Indah, Indah Rahmatika Sari; Effendi, Hansi; Lapisa, Remon; Wulansari, Rizky Ema
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.v12i4.3323

Abstract

Android-based learning media is one of the media that is easy to use by teachers and students in achieving learning objectives. This study aims to produce valid, practical, and effective learning media used for learning. The method used is Research and Development (R&D) with a 4-D development model (four D models). This study used an instrument in the form of a questionnaire. The questionnaires were divided into validation questionnaires, practicality, and effectiveness questionnaires. For data analysis, researchers used Aiken's formula and Gain Score. The validation sheet is filled out by 4 validators consisting of 2 material validators and 2 media validator. The practicality sheet is filled out by 30 students and 2 teacher. For effectiveness data used questions filled out by students. The questionnaire data obtained a validity level of 0.95 which indicates that the developed media is valid. For practicality, the result is 90% with the very practical category. The effectiveness of the developed media is seen from the value of students. After using the media, 100% of students had scores above the KKM, while from the gain score test, they got a value of 0.75 which means that the learning media developed can be said to be effective. Based on these data it can be concluded that android-based learning media in Information and Communication Technology (ICT) subjects is valid, practical, and effective for use as learning media.
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
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.
Sistem Prediksi Kelulusan Santri Tahfidz Qur’an Menggunakan Algoritma C4.5 Sriani; Juraidah, Juraidah
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.v12i4.3325

Abstract

Yayasan Tahfidz Qur’an Umul Mukminin Aisyah merupakan lembaga pendidikan penghafal Al-Qur’an yang setiap tahun kuota santri semakin bertambah. Namun, tidaklsemua santri dapat lulus tepat waktu sesuai masaabelajar yang ditempuhhsehingga mengakibatkannpenumpukkan santri yang tidakllulus sesuai masapperiodelkelulusanya. Penentuan kelulusan santri berdasarkan beberapa kriteria yang harus dilalui oleh santri selama menempuh pembelajaran dilYayasan Tahfidz Qur’an. Oleh karena itu, perlu dilakukan penelitian menggunakannteknik klasifikasi yanggdapat mengolahhdata dalam jumlahhbesar untuk menemukanppola yang terjadilpada dataasantri. Pengolahanndata tersebut digunakan untuk memprediksikkelas yang belumddiketahui yaitu prediksikkelulusan santri. Teknikkklasifikasi yanggdigunakan adalah decisionttreeedengan penerapanaalgoritmaaC4.5. Inputannyang digunakannberupa data santri yanggmeliputi dari prestasi, kedisiplinan, hapalan dan lafadz. Data santri yang digunakan adalah data sampelttraining yang sudah lulus pada tahun 2019 dengan jumlah data 170. Dimana berdasarkan hasil pengolahan, didapati 114 data santri lulus dan 56 tidak lulus.
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
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
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
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.

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