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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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Pengembangan Sistem Manajemen Antrian Berbasis E-Kiosk Dengan Metode Service Oriented Computing Di Perguruan Tinggi Hendri
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.v12i3.3230

Abstract

Untuk dapat mengelola layanan antrian memerlukan pengelolaan yang efisien dan juga efektif di perguruan tinggi. Kualitas layanan antrian akan berpengaruh kepada experience yang dirasakan oleh mahasiswa dan akan menjadi penentu kepuasan sehingga dapat berakibat fatal jika tidak diperbaiki dari sekarang, karena mahasiswa yang tidak puas terhadap layanan antrian kampus akan menceritakan experience yang buruk kepada teman, saudara, tetangga. Antrian pada Universitas Dinamika Bangsa saat ini masih menggunakan panggilan suara petugas sehingga seringkali kesulitan ketika antrian mahasiswa dalam jumlah banyak. Service-Oriented Computing (SOC) merupakan arsitektur perangkat lunak yang memungkinkan pembangunan aplikasi skalabel, fleksibel, dan dapat digunakan kembali dengan memecahkannya menjadi layanan kecil, independen, dan mandiri yang saling berkomunikasi melalui jaringan. Tujuan sistem manajemen antrian dengan E-kiosk ini adalah menyediakan layanan informasi dan transaksi secara elektronik melalui terminal interaktif yang terkoneksi dengan jaringan internet. E-kiosk dapat digunakan untuk memfasilitasi aktivitas akademis seperti pendaftaran, pembayaran uang kuliah, dan masalah antrian.
Support Vector Machine untuk Sentiment Analysis Bakal Calon Presiden Republik Indonesia 2024 Pranata, Boby; Susanti
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.v12i3.3231

Abstract

Survei Lingkaran Survei Indonesia dan Indo Barometer menempatkan 3 figur bakal calon presiden tertinggi, figur tersebut adalah Ganjar Pranowo, Anies Baswedan, dan Prabowo Subianto. Tiga tokoh yang dihasilkan dari survei tersebut merupakan figur terkenal. Banyak berita yang membicarakan tokoh tersebut, begitu juga dengan masyarakat. Masyarakat baik yang mendukung ataupun tidak, banyak membicarakan pada media sosial. Salah satu media sosial yang digunakan untuk beropini figur tersebut adalah media sosial Twitter. Dengan sentimen terhadap figur bakal calon presiden, dapat mengetahui bagaimana masyarakat menyikapi terhadap figur tersebut, trend sentimen terhadap figur, dan kata-kata yang menjadi sentimen. Sentimen-sentimen yang diberikan oleh pengguna Twitter tersebut banyak berupa opini ataupun berita yang jumlahnya sangat banyak di Twitter. Banyak teknik otomasi yang digunakan untuk melakukan analisis sentimen seperti pendekatan berbasis leksikon, menggunakan machine learning, atau gabungan keduanya. Algoritma machine learning yang digunakan beragam salah satunya Support Vector Machine (SVM). Hasil pelabelan menggunakan VADER Lexicon di dapatkan hasil sentimen positif 57.8% atau sebanyak 1022 ulasan, untuk sentimen netral 16.6% atau sebanyak 295 ulasan, dan untuk sentimen negatif 25.4% atau sebanyak 450 ulasan. Hasil pengujian menggunakan SVM menggunakan perbandingan 80% data latih 20% data uji menghasilkan akurasi sebesar 60%. Untuk perbandingan 70% data latih 30% data uji menghasilkan akurasi sebesar 59%. Sedangkan perbandingan 60% data latih 40% data uji menghasilkan akurasi sebesar 58%. Selanjutnya dapat disimpulkan hasil dari Confusion matrix bahwa pengenalan kalimat negatif lebih banyak dikenali sebagai kalimat positif. Berdasarkan hasil Word cloud kata capres, elektabilitas, pemilih, kritis, anis, dan ganjarmenangtotal merupakan kata yang sering muncul.
Enhancing Water Potability Assessment Using Hybrid Fuzzy-Naïve Bayes Azmi, Fadhillah; Gibran, M Khalil; Ridwan, Achmad
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.v12i3.3232

Abstract

In an effort to ensure a safe and high-quality water supply, the assessment of water potability is of paramount importance. An accurate and efficient assessment of water potability can be a challenge due to various influencing factors. Therefore, an innovative and integrated approach is needed to improve the assessment of water potability. In this study, we introduce a new approach to improving the assessment of water potability. This approach aims to overcome the shortcomings of traditional methods by using a hybrid fuzzy-Naïve Bayes approach to obtain a more accurate level of water potability. Fuzzy techniques are used to overcome uncertainty and ambiguity in the initial data. This method describes the probability weights in a fuzzy manner for various parameters. Then, the Naïve Bayes method is used to classify water samples based on the probability generated by the fuzzy system. This hybrid approach makes it possible to consider the relationship between parameters and generate more realistic probability values. This study uses datasets collected from various sources that include water potability parameters. A hybrid fuzzy-Naïve Bayes approach was then applied to this data set to make a more effective and accurate assessment of water potability. The experimental results show that the proposed method obtains an accuracy of 90%, which significantly improves the water potability assessment compared to the conventional method, which results in an accuracy of 84%. By combining fuzzy and Naïve Bayes techniques, we can overcome uncertainty in data and produce more accurate judgments.
Next Word Prediction for Book Title Search Using Bi-LSTM Algorithm Trigreisian, Alwizain Almas; Harani, Nisa Hanum; Andarsyah, Roni
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.v12i3.3233

Abstract

Finding a suitable book title is still quite difficult at the moment. We often guess what book title we want, but in reality the book title is often not available. This research aims to overcome these problems by producing an accurate and efficient prediction model in predicting the next words in book title search using a deep learning algorithm, namely Bidirectional Long Short Term Memory (Bi-LSTM). The research stages consist of data collection, data preprocessing, data modeling, evaluation, and implementation. This research uses a dataset of Indonesian book titles obtained from the bukukita.com online bookstore website with 5618 data. The results show that the resulting deep learning model can predict the next words in the book title search with an accuracy of 81.82%. The model is implemented in the form of a web application using the Django framework, Python language, and MySQL database.
Perancangan Website E-Voting Menggunakan Smart Contract Pada Blockchain Polygon Budi, Eko Yanuarso; Prihantoro, Cahyo; Nugroho, Nicolaus Euclides Wahyu
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.v12i3.3234

Abstract

Electronic voting (e-voting) merupakan salah satu jenis sistem voting yang prosesnya berjalan dengan sistem elektronik. E-voting dikembangkan untuk menjadi alternatif lain voting tradisional negara demokrasi. Di Indonesia sistem pemilihan menggunakan e-voting mulai diterapkan pada skala desa. Dengan adanya perancangan website e-voting menggunakan smart contract blockchain Polygon bertujuan agar melengkapi sistem yang sudah ada terutama keamanan, transparansi dan meningkatkan kepercayaan masyarakat dalam proses pemilihan. Penerapan blockchain pada masa sekarang masih dibilang awal tentunya membutuhkan pengembangan dan improvisasi. Mekanisme yang ditawarkan pada penelitian ini adalah penggunaan smart contract voting yang artinya proses voting berjalan diatas jaringan blockchain. Pemilih akan mendapatkan Non-Fungible Token setelah voting sukses sebagai bukti telah memilih.
Klasifikasi Penyakit Bawang Merah Menggunakan Naive Bayes dan CNN dengan Fitur GLCM Arfah, Jumrayanti; Purnawansyah; Darwis, Herdianti; Sastra, Ramdan
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.v12i3.3236

Abstract

Tanaman bawang merah merupakan salah satu tanaman penting dalam industri pertanian. Penyakit pada tanaman bawang merah dapat mengakibatkan kerugian yang signifikan bagi petani dan produsen. Penelitian ini bertujuan untuk mengklasifikasikan penyakit bawang merah pada daun bawang merah yang disebabkan oleh bercak ungu dan moler. Pengumpulan data citra bawang merah dilakukan secara langsung yang dilanjutkan dengan tahap pre-processing sebelum pengklasifikasian penyakit pada tanaman bawang merah. Algoritma Naive Bayes dan CNN dengan ekstraksi fitur GLCM digunakan dalam penelitian ini untuk melakukan perbandingan klasifikasi antara dua metode tersebut dalam mengklasifikasikan penyakit tanaman bawang merah yaitu bercak ungu dan moler. Hasil pengujian dengan menggunakan citra sebanyak 160 penyakit moler dan 160 penyakit bercak ungu menunjukkan bahwa kedua algoritma klasifikasi Naive Bayes dan CNN dengan ekstraksi fitur GLCM mampu mengklasifikasikan penyakit moler dan penyakit bercak ungu pada daun bawang merah dengan akurasi yang baik sebesar 100%. Onion plants are one of the important crops in the agricultural industry. Diseases in onion plants can result in significant losses for farmers and producers. This research aims to classify onion diseases on onion leaves caused by priole blotch and molāris. The of onion image data colaction was performed directly, followed by a pre-processing stage before classifying diseases in onion plants. The Naive Bayes algorithm and CNN with GLCM feature extraction are used in this study to compare the classification between the two methods in classifying onion diseases. The test results using a total of 160 priole blotch and 160 molāris diseases show that both the Naive Bayes and CNN classification algorithms with GLCM feature extraction are capable of classifying priole blotch and molāris diseases on onion leaves with a perfect accuracy of 100%.
Sentiment Analysis of Tweets About Allowing Outdoor Mask Wear Using Naïve Bayes and TextBlob Ilham Firman Ashari; A, Fadhillah; M. Daffa; Sekar A
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.v12i3.3238

Abstract

Covid-19, a virus that attacks the respiratory tract and has a fairly high mortality rate, has spread throughout the country. On March 11, 2020, WHO declared Covid-19 a global pandemic. The government is trying various efforts to reduce the number of sufferers of this virus. Starting from the implementation of the lockdown, PPKM, to making Government Regulations related to the use of masks and so on for personal protection. In June 2021, there was a spike in Covid-19 cases in Indonesia and Covid-19 patients increased drastically. Conditions at that time were very chaotic, and left trauma for some people. On May 17, 2022, the government made concessions in the use of masks in open spaces while maintaining social distance. Even though masks play an important role in preventing the spread of the virus. With this, a research related to "Analysis of Sentiment on Tweets regarding Allowance for the Use of Masks in Outdoors using Naive Bayes was carried out" to find out public opinion. The research was conducted using Text Mining through Twitter sentiment and Naive Bayes for classification. Based on research, the majority of twitter users give a neutral response. This is indicated by the number of neutral sentiments of 75.76% or about 757 tweets. The data used in this study, namely 1000 Indonesian tweets with the keyword 'jokowi mask'. Testing data of 20% resulted in a more accurate model, which resulted in an accuracy of about 85%, while the model using testing data of 30% only produced an accuracy of about 83%.
Goal-Oriented Modeling of an Urban Subway Control System Using KAOS Kadakolmath, Lokanna; D. Ramu, Umesh
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.v12i3.3239

Abstract

The extent to which a safety-critical system, such as an urban subway control system, accomplishes its goals is a fundamental metric of its success. Identifying and assessing these goals should thus be one of the primary tasks in safety-critical system development. The breakdown of these systems may result in the loss of human lives and assets. The failure of these systems is caused by insufficient, incomplete, ambiguous, or conflicting requirements. Non-functional requirements are also separated from requirement specifications. Goal-oriented requirements engineering methodologies, such as KAOS, are used to tackle these challenges by providing adequate, complete, unambiguous, and consistent requirements in terms of goals. As a result, the KAOS approach is utilized in this article to construct a goal-oriented model of an urban subway control system. Variability and obstacle concerns are also addressed in this study.
Perancangan Sensor Terdistribusi untuk Pendeteksi Gempa Bumi Menggunakan Protokol Komunikasi MQTT Ichwana Putra, Dody; Ekariani, Shelvi
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.v12i3.3241

Abstract

Sebagai ibu kota Provinsi Sumatera Barat, Kota Padang memiliki risiko tinggi terhadap gempa bumi dan tsunami karena letaknya di antara dua lempeng benua dan adanya Patahan Semangko. Beberapa tempat perlindungan di Padang berfungsi sebagai lokasi evakuasi dan penyelamatan saat terjadi tsunami. Paper ini menyajikan sebuah sistem pendeteksi gempa bumi yang menggunakan sensor terdistribusi. Identifikasi gempa bumi dilakukan dengan menghitung nilai Peak Ground Acceleration (PGA) dari gelombang P dan gelombang S menggunakan sensor piezoelektrik dan akselerometer. Sistem yang diusulkan merupakan jaringan sensor node terdistribusi yang berkomunikasi menggunakan protokol MQTT. Untuk mengevaluasi kinerja sistem, kami mengimplementasikannya menggunakan Raspberry Pi, sensor piezoelektrik, akselerometer MPU-6050, dan modul Xbee untuk komunikasi data. Hasil eksperimen menunjukkan bahwa sistem ini dapat mendeteksi magnitudo dan intensitas gempa bumi dengan akurat.
The Influence of Tutorial-Based Learning Model on Information and Communication Technology (ICT) Subjects at Junior High School Painan Helmiza; Ganefri; Ambiyar; Irfan, Dedy
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.v12i3.3242

Abstract

This study aims to determine the effect of learning outcomes using a tutorial-based learning model in Information and Communication Technology (ICT) subjects at Junior High School 02 Painan. This study uses a quasi-experimental research method. The population in this study were students of class VIII Junior High School 02 Painan. The sample in this study used a random sampling technique with students in class VIII A as the experimental class and VIII B as the control class with 30 students in class A and 25 students in class B. The research instrument used was objective questions which were tested for validity, reliability, discriminatory power, and level of difficulty. Data were analyzed with parametric statistics including normality test, homogeneity test and hypothesis testing. As well as to test the activity and response of the research instrument used was a questionnaire which was validated by three lecturers who are experts in learning evaluation and the results of effectiveness were analyzed based on classical completeness and based on the Gain Score. The results of this study revealed that student learning outcomes using the tutorial-based learning model with an average score of the experimental class was 78.933 better than ordinary learning with an average score of the control class was 69.76 in the final test analysis where the tcount was 9.484 and ttable is 1.68 with a significance level of 95% so that tcount > ttable and the research hypothesis is accepted. As well as seen from the activeness and response of students, the average result of activity is 80.9% with a very good classification and for the average student response result is 87.4% with a very good classification result. The effectiveness of learning using a tutorial-based learning model in the effectiveness trial, obtained 87% of students scored ≥ 70 which stated classically effectiveness and for the control class obtained 43.33% of students scored < 70 which stated that it was not classically effective. And in terms of Gain Score gets a value of 37.31% (medium). Thus it can be concluded that there is an influence of the tutorial-based learning model on Information and Communication Technology (ICT) learning on student learning outcomes.

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