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All Journal JIKO (Jurnal Informatika dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Petir Computer Science (CO-SCIENCE) Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Klik - Jurnal Ilmu Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Prioritas : Jurnal Pengabdian Kepada Masyarakat Mestaka: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Engineering: Progress, Application and Technology MDP Student Conference Journal of Embedded Systems, Security and Intelligent Systems Jurnal Sistem Informasi Komputer dan Teknologi Informasi Jurnal Teknik Indonesia (JU-TI) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Daniel Udjulawa
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Other

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Perancangan Aplikasi Simulasi Penyelamatan Diri Dari Gempa Bumi Marzali, Azizurrahman; Udjulawa, Daniel; Yoannita, Yoannita
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 2 (2021): April 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (958.577 KB) | DOI: 10.35957/algoritme.v1i2.892

Abstract

Menurut The World Risk Index tahun 2019, Indonesia berada pada peringkat 37 dari 180 negara paling rentan bencana. Pada tanggal 5 Agustus 2018 gempa bumi di Lombok menelan korban sebanyak 259 orang meninggal dunia, dan 1.033 mengalami luka berat. Kurangnya kesiap siagaan dan edukasi mengenai bencana gempa bumi menjadi salah satu faktor penyebab banyaknya jumlah korban. Maka dari itu dibuatlah sebuah aplikasi simulasi yang ditujukan untuk mengedukasi masyarakat supaya dapat mengetahui apa saja yang harus dilakukan pada saat terjadi gempa bumi. Aplikasi ini dibuat menggunakan metode prototyping untuk melakukan identifikasi masalah yang ada pada setiap kejadian gempa bumi. Tujuan dari pembuatan aplikasi ini adalah untuk memberikan pengetahuan tentang bagaimana cara menyelamatkan diri dari gempa bumi. Aplikasi ini berbentuk game yang mempunyai sudut pandang First Person yang mempunyai empat stage dan setiap stage mempunyai beberapa misi. Pemain harus menyelesaikan seluruh misi pada setiap stage agar dapat melanjutkan ke stage selanjutnya. Hasil dari penelitian ini yaitu menghasilkan sebuah aplikasi simulasi dalam cara menyelamatkan diri dari gempa bumi. Berdasarkan uji Black-Box yang telah dilakukan, diperoleh hasil uji coba bahwa aplikasi ini dapat dijalankan dengan baik dan sesuai dengan tujuan.
Klasifikasi Penyakit Daun Sawit Menggunakan Metode Jaringan Saraf Tiruan Dengan Fitur Local Binary Pattern Simanjuntak, Andreas Jeremy Obet; Udjulawa, Daniel
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v3i1.3158

Abstract

Diseases on palm leaves are diseases caused by bacteria or fungi. One way to find out diseases on palm leaves is to observe the pattern on the surface of the palm leaves. The pattern on the palm leaves will be analyzed by an expert to find out whether there is disease on the palm leaves or not. This study aims to classify oil palm leaves whether there is disease or not on oil palm leaves by using a program. The right method is needed to produce good accuracy, the researcher uses the ANN (Artificial Neural Network) classification method and the LBP (Local Binary Pattern) extraction method. The steps carried out on the image before being classified are Grayscale, then extraction using LBP (Local Binary Pattern) and classification using ANN (Artificial Neural Network) using 17 train functions with the result that 5 neurons get an average accuracy of 81%, precision 95 %, and 94% recall. In 10 neurons get an average of 95% accuracy, 97% precision, and 96% recall. And the 20 neurons get an average of 97% accuracy, 97% precision, and 96% recall. Keywords: Palm leaf disease, LBP, ANN, neuron
Identifikasi Penyakit Pada Tanaman Kopi Berdasarkan Citra Daun Menggunakan Metode Convolution Neural Network Fatchurrachman, Ahmad; Udjulawa, Daniel
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 2 (2023): April 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v3i2.3384

Abstract

Coffee plants are usually made for drinks made from coffee beans that have been ground into powder. One of the causes of decreased coffee quality is caused by pests that can attack from the leaves, stems and roots. This study aims to identify coffee plant diseases based on leaves using the Convolution Neural Network (CNN) method with the ResNet-50 architecture with the Adam optimizer. The total data from the dataset is 1664 images, in the dataset there are 1264 train data images and 400 test images. The highest result in training in this study using 60 epochs and Adam's optimizer with a probability value of learning_rate of 0.0001 getting a probability value of 0.9969 and the lowest value getting a probability value of 0.4918. The results of testing the test data in this study obtained an accuracy rate of 99%.
Deteksi Masker Melalui Video CCTV Menggunakan You Only Look Once Darmawan, Dean; Udjulawa, Daniel; Wijaya, Novan
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 2 (2023): April 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v3i2.3598

Abstract

The coronavirus pandemic or known as the COVID-19 pandemic is a global pandemic of corona virus that are caused by severe acute respiratory syndrome coronavirus 2 that are started in Wuhan, China in 2019. In 30th January 2020 World Health Organization (WHO) declared an emergency situation towards COVID-19 and in 11th March 2020, WHO officially declared an ongoing global pandemic of COVID-19. COVID-19 cases in the world itself is already reaching 181 million of cases with around 3.92 million deaths. Indonesia itself is one of the country that are affected by COVID-19 spread with 2.09 million cases and 56,729 deaths. In order to decrease the amount of COVID-19 cases, WHO require each individuals to do social distancing, stay hygiene, and always wearing face mask to prevent even more spread of the virus. A method to do face mask detection is proposed using a object detection method, You Only Look Once (YOLO). The test results obtained by calculating f-measure with the highest accuracy of 0.59 and the lowest of 0.19 using CCTV video that are taken with 70 cm distance. In the second test using video that are recorded with more than 90 cm the program obtained it’s result of 0.
Implementasi Algoritma K-Nearest Neighbor untuk Klasifikasi Cuaca Dandy, Dandy; Udjulawa, Daniel; Yohannes, Yohannes
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 1 (2023): Oktober 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i1.4932

Abstract

Weather is a brief natural event concerning the atmospheric conditions that take place on Earth which are determined by pressure, wind speed, temperature, and air phenomena. This study classifies 3 weather classes, namely sunny, cloudy, and rainy using the K-Nearest Neighbor algorithm as a weather classification algorithm with K value parameters of 3, 5, 7, and 9. Weather dataset 96.453 data to be examined is data taken from the Kaggle website. The dataset is divided into training data and test data with a ratio of 80:20. The implementation of the K-Nearest Neighbor algorithm produces a confusion matrix and classification report where in the confusion matrix, the largest number of correctly predicted data is at the value K = 9, namely 13.132 correctly predicted data with the largest number of correctly predicted data in the cloudy class, namely 10.865 data. As for the classification report, the highest accuracy value for both the cloudy, rainy, and sunny weather classes is at K = 9, which is 68.073%, and the highest precision, recall, and f1-score values are found in the cloudy class at K = 9, respectively contributed 72.095%, 89.288%, and 79.775%.
Workshop Pembuatan Feed Instagram Taruna Siaga Bencana Di Dinas Sosial Provinsi Sumatera Selatan Rahman, Abdul; Udjulawa, Daniel; Mulyati, Mulyati
FORDICATE Vol 1 No 1 (2021): November 2021
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (507.156 KB) | DOI: 10.35957/fordicate.v1i1.1625

Abstract

The purpose of the implementation of this workshop can provideinsight to the TAGANA South Sumatra team in the creation of content that willbe uploaded on sosial media, one of which is Instagram. The use of Instagram,the majority of which is to use videos and pictures is an interesting thing to beable to convey and even promote activities from TAGANA South Sumatera. Theapplication used is Adobe Photoshop. In this activity using discussion methods,by applying this discussion method can provide training and mentoring. So thatthe participants can be closer and feel interested so that it is easily accepted bythe participants. The final result of this workshop the participants can movetheir creativity in creating an Instagram feed so that it attracts the attention ofthe public.
Identify the Maturity Level of Apples Using Fuzzy Logic Mamdani Zulnardi, Andre; Udjulawa, Daniel
Jurnal Teknik Indonesia Vol. 2 No. 02 (2023): Jurnal Teknik Indonesia (JU-TI), Desember 2023
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-ti.v2i02.545

Abstract

Apples are one type of fruit that has properties including preventing disease, nourishing the body and being a menu when running a diet. This study aims to develop an identification system for the maturity level of apples using the mamdani fuzzy logic method. Fuzzy logic mamdani is a fairly good method of identification because the classes to be used have been predetermined. In this study, the apples used were Rome Beauty apples. The maturity level is based on the color which is divided into two, namely green raw and reddish yellow ripe. Data processing is done by preprocessing images such as resizing fruit directly. The accuracy of the dataset measured using this method results in an accuracy of 96%. In this study, an analysis of the input and output features needed by Mamdani's fuzzy logic was also carried out in classifying the maturity level of apples. The results showed that the input data could not be used effectively to classify the maturity level of apples due to the lack of input types used.
Klasifikasi Penyakit Daun Kelapa Menggunakan Xception Pada Data Imbalanced Dengan Smote Muhammad Totti Alfarabi; Daniel Udjulawa
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 6 No. 1 (2026): June 2026
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v6i1.17118

Abstract

The decline in coconut production due to Weligama Coconut Leaf Wilt Disease (WCLWD) and Coconut Caterpillar Infestation (CCI) can be detected using deep learning. However, previous studies have largely ignored extreme data imbalance ratios, leaving models vulnerable to pseudo-accuracy and failure in recognizing minority classes. Furthermore, no existing studies on coconut disease classification have specifically evaluated model robustness against visual anomalies and background bias. To fill this gap, this study not only integrates the Xception architecture with the SMOTE oversampling technique to overcome imbalanced data but also conducts comprehensive stress testing. Using 5,139 images distributed in a 70:15:15 ratio, SMOTE was specifically applied to the training data. The model was optimized using a 299x299 resolution, a learning rate of 0.00001, and a 0.5 Dropout layer. Testing demonstrated optimal results with an overall accuracy of 99%. The implementation of SMOTE successfully handled data imbalance without sacrificing the sensitivity of the minority class (healthy leaves), evidenced by a 0.95 Recall and 0.82 F1-Score. Moreover, as a novel evaluation, testing using anomalous Out-of-Distribution images revealed a background bias in the CCI class. Nevertheless, the low predictive confidence level (43.06%) confirms that the model's regularization effectively prevents overconfident predictions and optimally calibrates visual uncertainty.
The Sentiment Analysis of Public Comments on “Program Makan Siang Gratis” Using KNN (K-Nearest Neighbor) and SMOTE Algorithm Vincent Vincent; Vincentius Hansel Irsansaputra; Daniel Udjulawa
CEPAT Journal of Computer Engineering: Progress, Application and Technology Vol 5 No 01 (2026): May 2026
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cepat.v3i02.7734

Abstract

This research analyzes public sentiment on “Program Makan Siang Gratis” using the KNN algorithm, enhanced with the SMOTE technique, to provide insights and recommendations for policymakers aiming to achieve “Indonesia Emas 2045”. The study employs Google Colab and Python for testing. KNN is used for sentiment analysis and classification, with SMOTE addressing data imbalance. Results from two scenarios without SMOTE and with SMOTE show that performance is more optimal without SMOTE, as SMOTE decreases performance by 34%. The k4 parameter yields the best results: 76% accuracy, 57% precision, 77% recall, and 65% F1-Score. Analysis of comments from the “tempodotco” YouTube channel reveals that public sentiment towards the program proposed by President Prabowo Subianto and Vice President Gibran Rakabuming Raka is predominantly negative
PELATIHAN PEMBUATAN WEBSITE BERBASIS CMS WORDPRESS DI PUSAT KEGIATAN BELAJAR MASYARAKAT SEKOLAH BINTANG Shela Shela; Fanny Krisvyanti; Michael Roda Robert; Victoria Valensita Robert; Andreyas Andreyas; Muhyi Haadi Sewithto; Daniel Udjulawa
FORDICATE Vol 5 No 1 (2025): November 2025
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v4i3.12184

Abstract

Abstract: Effective information management and improved access to educational programs remain key challenges for non-formal educational institutions such as PKBM Sekolah Bintang. To address these issues, this community service activity was designed to enhance the digital skills of PKBM administrators through training in the creation and management of a CMS-based WordPress website. The training was conducted in an interactive and practical manner, with PKBM students as the main participants. Evaluation results indicated a significant improvement in participants’ ability to independently manage the website, from installation and design customization to digital content management. The website now serves as an effective communication and promotional tool to broaden the reach of the institution’s educational programs. However, continued training and technical support are still needed to optimize website management and ensure the sustainability of the institution’s digital capacity. Overall, this initiative contributes significantly to strengthening the use of information technology in non-formal education. Abstrak: Pengelolaan informasi yang efektif serta peningkatan akses terhadap program pendidikan menjadi tantangan utama bagi lembaga pendidikan non-formal seperti PKBM Sekolah Bintang. Untuk mengatasi hal ini, kegiatan pengabdian masyarakat ini dirancang guna meningkatkan kemampuan digital siswa PKBM melalui pelatihan pembuatan dan pengelolaan website berbasis CMS WordPress. Pelatihan yang berlangsung secara interaktif dan praktis melibatkan murid PKBM sebagai peserta utama. Evaluasi hasil pelatihan menunjukkan adanya peningkatan kemampuan peserta dalam mengelola website secara mandiri, mulai dari instalasi, pengaturan tampilan, hingga pengelolaan konten digital. Saat ini, website tersebut berfungsi sebagai media komunikasi dan promosi yang efektif untuk memperluas jangkauan informasi program pendidikan kesetaraan. Meski demikian, pelatihan lanjutan serta pendampingan teknis tetap diperlukan guna mengoptimalkan pengelolaan website dan mendukung keberlanjutan peningkatan kapasitas digital lembaga. Secara keseluruhan, kegiatan ini memberikan kontribusi penting dalam memperkuat pemanfaatan teknologi informasi pada pendidikan non-formal.
Co-Authors Abdul Rahman Andreyas Andreyas Anggoro Aryo P. Dafid Dafid Dafid Dafid Dandy, Dandy Darmawan, Dean Dorie P. Kesuma Famerdi, Farhan Agung Fanny Krisvyanti Fatchurrachman, Ahmad Febbiola Febbiola Febbiola, Febbiola Fellycia Caroline Fransiska Prihatini Sihotang Hartati, Ery Intan Cahya Sucita Ja'far, Ja'far Jonathan Jason Constantine Kadafi, Aldo Kasanova, Sinyo Kurniawan, Marthin Hokita Lisa Amelia Fransen M Fajri Septian Dwi Cahyo Martin, Nicolas Marzali, Azizurrahman Michael Roda Robert Muhammad Fawzan Muhammad Haviz Irfani Muhammad Rizky Pribadi Muhammad Totti Alfarabi Muhyi Haadi Sewithto Mulyati Mulyati Mulyati Mulyati Novan Wijaya Nyimas Artina Oktarina, Serly Pramudiya, Relin Ramadhan, Ahmad Wildan Rizky Raphael Lee Regiza Giovanno Rizky Ridho Ramadhan Serenity Devina Suryanto Setiawan, Jerry Shela Shela Simanjuntak, Andreas Jeremy Obet Sutanto, Steven Yesua Timoteus Ivan Sariyo Victoria Valensita Robert Vincent Vincent Vincentius Hansel Irsansaputra William William Wiwik Handayani Yoannita, Yoannita Yohannes, Yohannes Zulnardi, Andre