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All Journal ComEngApp : Computer Engineering and Applications Journal ELKHA : Jurnal Teknik Elektro JSI: Jurnal Sistem Informasi (E-Journal) Annual Research Seminar Dinamisia: Jurnal Pengabdian Kepada Masyarakat PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) CCIT (Creative Communication and Innovative Technology) Journal Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Kanda Januar Miraswan
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Journal : Sriwijaya Journal of Informatics and Applications

 1 
Diagnosis Of Respiratory Tract Infections In Toddlers With Expert System Using Variable-Centered Intelligent Rule System And Certainty Factor Method Ahmad Gustano; Abdiansah Abdiansah; Kanda Januar Miraswan
Sriwijaya Journal of Informatics and Applications Vol 2, No 1 (2021)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Expert system can help the experts in diagnose the Respiratory TractInfection For Toddlers. This research have a purpose to build anexpert system for Android with Kotlin language using Variable-Centered Intelligent Rule System and Certainty Factor method, alsoget the accuracy of it. System’s input is a yes or no answer from Yes-No Question with user. This research use 164 patient data of toddlersat UPTD Kenten Laut Banyuasin Health Center and variables which issymptoms that occurs in toddlers such as cough, cold, hard to breathe,fever, and the results of a physical examination conducted by theexpert. Based on test result, the system has 95,52% accuracy whendiagnose ISPA case, and 100% accuracy when diagnose Pneumoniacase. So, it can be concluded that Variable-Centered Intelligent RuleSystem and Certainty Factor method can be used to diagnoserespiratory infections in toddlers.
Prediction of the Number of New Cases of Covid-19 in Indonesia Using Fuzzy Time Series Model Chen Kanda Januar Miraswan; Wiwik Anum Puspita; Alvi Syahrini Utami
Sriwijaya Journal of Informatics and Applications Vol 3, No 1 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i1.35

Abstract

Coronavirus Diseases 2019 (Covid-19) is a disease caused by a virus that originated in Wuhan, China. This virus infects people rapidly to the country of Indonesia. According to the latest Covid-19 Development Team in Indonesia, as of 09/08/2021, there were around 3,686,740 people who were confirmed positive for Covid-19. With the numbers continuing to grow, predictions of new cases of Covid-19 in Indonesia were made using the time series method. The method used by the researcher is Chen's Fuzzy Time Series. The purpose of the researcher is to forecast, to find out the prediction of the number of new cases of Covid-19 in Indonesia using the FTS Chen method into software. In addition, in order to provide information to predict, so that the government knows and can make decisions. To measure the performance of the method, the Mean Absolute Percentage Error (MAPE) is used as a measure of the level of accuracy of the forecasting performed. The test data used were 363 data with several variations of parameters  & . From the results of the analysis that was tested by the researcher, with 50 trials of parameter input, better accuracy results were obtained at input  = 135135 and  = 2000 with MAPE is 35.55006797 (35%).
Identification Types Of Student Learning Modalities In Physics Subjects With Expert Systems Using Bayes Theorem Method Muhammad Ukkasyah; Yunita Yunita; Kanda Januar Miraswan
Sriwijaya Journal of Informatics and Applications Vol 4, No 1 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i1.54

Abstract

Learning modality is a person's way of absorbing and processing information effectively and efficiently. This study aims to determine the results of the identification types of student learning modalities in physics subjects with an expert system using the Bayes theorem method, and the accuracy of the Bayes theorem method in identifying types of student learning modalities in physics subjects. This study uses the Bayes theorem method because it can produce a parameter estimate by combining information from the sample and other information that has been previously available to determine the results of the learning modality. This study uses 21 characteristics of learning modalities, 3 types of learning modalities, and 30 test cases obtained from an expert physics teacher at SMA Sumsel Jaya Palembang. Based on the tests that have been carried out, the results show that the system has an accuracy of 90% in identifying types of student learning modalities in physics subjects. It can be concluded that the Bayes theorem method can be used to identify types of student learning modalities in physics subjects.
Sentiment Analysis Using PSEUDO Nearest Neighbor and TF-IDF TEXT Vectorizer Yogi Pratama; Abdiansyah Abdiansyah; Kanda Januar Miraswan
Sriwijaya Journal of Informatics and Applications Vol 4, No 2 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i2.68

Abstract

Twitter is one of the social media that is often used by researchers as an object of research to conduct sentiment analysis. Twitter is also a good indicator in influencing research, problems that often arise in research in the field of sentiment analysis are the many factors such as the use of colloquial or informal language and other factors that can affect sentiment results. To improve the results of sentiment classification, it is necessary to carry out a good information extraction process. One of the word weighting methods resulting from the information extraction process is the TF-IDF Vectorizer. This study examines the effect of the TF-IDF Vectorizer weighting results in sentiment analysis using the Pseudo Nearest Neighbor method. The results of the f-measure classification of sentiment using the TF-IDF Vectorizer at parameters k-2 = 89%, k-3 = 89%, k-4 = 71% and k-5 = 75% while without using the TF-IDF Vectorizer on the parameters k-2 = 90%, k-3 = 92%, k-4 = 84% and k-5 = 89%. From the results of the classification of sentiment analysis that does not use the TF-IDF Vectorizer, the f-measure value is slightly better than using it.
Expert System to Diagnose Disease in Toddlers Using Dempster Shafer Method septi ana; Novi Yusliani; Kanda Januar Miraswan
Sriwijaya Journal of Informatics and Applications Vol 2, No 2 (2021)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v2i2.27

Abstract

Children, especially toddlers at the age of two months to five years old are more susceptible to disease. Limited information about diseases that attack children makes it difficult for parents to predict the disease that will suffer from their children. Therefore we need an expert system  that can predict the disease suffered by children, and the method used in this study is the Dempster Shafer method. The Dempster Shafer method can be implemented into an expert system to combine separate symptoms (evidence) in calculating the probability of a disease. Based on the test results using 250 test data, the accuracy of the expert system for diagnosing diseases in children under five years old using Dempster Shafer method is 94%.Keywords : Expert System, Dempster Shafer, Disease in Toddlers
Classification of Emotions on Twitter using Emotion Lexicon and Naïve Bayes Dhiya Fairuz; Novi Yusliani; Kanda Januar Miraswan
Sriwijaya Journal of Informatics and Applications Vol 2, No 2 (2021)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v2i2.24

Abstract

Social media is a means of interaction and communication. One of the social media that is often used is Twitter. Twitter allows its users to express many things, one of which is being a personal media to provide various kinds of expressions from its users such as emotions. Users can express their emotions and sentiments through writing on the status of their social media posts. One method to find out the emotion in the sentence is using the Emotion Lexicon. However, the lexicon-based method is not good at classifying data because not every word contains emotion. So, there's a need to combine it with other classification method such as Naive Bayes. Naïve Bayes relies on independent assumptions to obtain a classification through the probability hypothesis that each class has. The results of the classification test with Emotion Lexicon alone have 46% accuracy, 45% precision, 51% recall and 36% f-measure. While the results of the classification test with Emotion Lexicon and Naïve Bayes resulted in an accuracy of 65%, precision of 77%, recall of 55%, and f- measure of 59%.
Comparison Of The Results Of The Jaccard Similarity And KNearest Neighbor Algorithms Using The Case Based Reasoning (CBR) Method On An Expert System For Diagnosing Pediatric Diseases Hidayatullah, Altundri Wahyu; Rini, Dian Palupi; Arsalan, Osvari; Miraswan, Kanda Januar
Sriwijaya Journal of Informatics and Applications Vol 5, No 1 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i1.55

Abstract

Health ranks highest in supporting the continuity of every human activity, especially children. The availability of a doctor is still relatively lacking, especially in remote areas. This makes people have difficulty in diagnosing certain diseases so that medical treatment becomes too late and can even be fatal for the patient. So it is necessary to create a system that has the ability to be able to diagnose diseases in children like an expert. The method used in this study is Case Based Reasoning (CBR) with the Jaccard Similarity Algorithm and K-Nearest Neighbor. Jaccard Similarity is one way to calculate the similarity of two objects (items) which are binary. Similarity calculations are used to generate values whether or not there is a similarity between new cases and existing cases in the case base. While the K-Nearest Neighbor (KNN) Algorithm belongs to the instance-based learning group. The KNN algorithm allows the program to find old cases that are most similar to the current case. Based on the test results using 50 sample data, the expert system can provide diagnostic results in accordance with expert diagnoses. The accuracy results for the K-Nearest Neighbor Algorithm are 72% while the accuracy results for the Jaccard Similarity Algorithm are 70%.
Optimization of Tsukamoto FIS Using Genetic Algorithm for Rainfall Prediction in Banyuasin Regency Akbar, Muhammad Rafi; Miraswan, Kanda Januar; Rodiah, Desty; Buchari, Muhammad Ali; Marjusalinah, Anna Dwi
Sriwijaya Journal of Informatics and Applications Vol 5, No 2 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i2.118

Abstract

Indonesia, as a tropical country with high rainfall, heavily relies on accurate rainfall predictions for various critical purposes, including water resource management and extreme weather impact mitigation. One commonly used method is the Tsukamoto Fuzzy Inference System (FIS). However, implementing the Tsukamoto FIS often leads to high error rates. This is attributed to the difficulty in determining the boundaries of fuzzy variable membership functions. To address this issue, this research proposes an innovative approach by optimizing the boundaries of fuzzy membership functions using Genetic Algorithms (GA). The study resulted in a 49.02% reduction in the error rate, decreasing from 76.82% to 27.8%. This method significantly enhances rainfall prediction accuracy and contributes to the advancement of more sophisticated prediction methods. The optimization method proposed in this study also holds potential for application across various atmospheric science contexts.
Subject scheduling system using Ant Colony Optimization at MAN 3 Palembang Al Ashri, Muhammad Rizky; Miraswan, Kanda Januar; Darmawahyuni, Annisa; Utari, Meylani
Sriwijaya Journal of Informatics and Applications Vol 5, No 2 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i2.119

Abstract

In preparing the subject schedule, it must be done correctly because all teaching and learning activities are between teachers and students. So far, the subject scheduling process at MAN 3 Palembang is carried out manually so that clashes often occur between subjects and teachers who teach can teach in different classes at the same time resulting in the teaching and learning process being slightly disrupted. One of the common metaheuristic algorithms The solution used for optimization problems is the Ant Colony Optimization algorithm or commonly known as the ant algorithm. The application system or users of this application to create schedules using the Ant Colony Optimization algorithm method is useful for operators who create schedules in schools. This system can also be applied in cases where schedules conflict, namely teachers teaching in the same room and teachers teaching the same subject teaching in different classes at the same hours. This makes it easier for operators to create schedules so that they can be resolved more easily and quickly. This application was successfully developed into a subject scheduling system and managed to run optimally. From the results of implementing scheduling using the Ant Colony Optimization algorithm method used in compiling subject rosters, it can help the MAN 3 Palembang school which previously carried out schedule preparation manually.
Co-Authors Abdiansah Abdiansah, Abdiansah Abdiansyah Abdiansyah Ahmad Fali Oklilas Ahmad Gustano Akbar, Muhammad Rafi Al Ashri, Muhammad Rizky Al Farissi Alvi Syahrini Utami Anita Desiani Anna Dwi Marjusalinah Ardani, Vanya Terra Arhami, Muhammad Ari Wedhasmara Arief Wijaya Auzan Lazuardi Buchari, Muhammad Ali Darmawahyuni, Annisa Dellin Irawan Desty Rodiah Dhiya Fairuz Dian Palupi Rini Eni Triningsih Firdaus Firdaus Hidayatullah, Altundri Wahyu Irawan, Dellin Junia Kurniati Junia Kurniati, Junia Kartika Rahmayani M. Fachrurrozi . Marjusalinah, Anna Dwi Mastura Diana Marieska Meylani Utari Muhammad Fachrurrozi Muhammad Fachurrozi Muhammad Husein Nashr Muhammad Ukkasyah Novi Yusliani Osvari Arsalan Redo Jufarda Rendyansyah Sarmayanta Sembiring septi ana Sri Indra Maiyanti Vanya Terra Ardiani Wiwik Anum Puspita Yogi Pratama YUNITA Yunita Yunita Yunita Yunita