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All Journal ComEngApp : Computer Engineering and Applications Journal Jupiter Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Alvi Syahrini Utami
Teknik Informatika, Fakultas Ilmu Komputer, Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Journal : Sriwijaya Journal of Informatics and Applications

 1 
Determining The Quality and Production of Fresh Vegetables Using Simple Multi - Attributes Rating Technique (SMART) - Fuzzy Tsukamoto Dedi Irawan; Alvi Syahrini Utami; Desty Rodiah
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

Vegetables are one of the most important needs in Indonesia. This is due to the increasing need for healthy food to meet daily needs. With the need for vegetables, the quality and production process are still hampered because it is done manually. Therefore created a system that can help someone determine the quality and production of the right vegetables. This system uses the SMART method and fuzzy Tsukamoto with the criteria and variables of vegetables used to get good quality and production. The SMART and fuzzy Tsukamoto method used a dataset of 20 vegetable commodities. In this study, 4 criteria and 3 variables were used, namely height, soil pH, temperature and age of harvest for quality determination. The production uses the variables of demand, supply and production.
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%).
Cat Breeds Classification Using Convolutional Neural Network For Multi-Object Image Naura Qatrunnada; Muhammad Fachrurrozi; Alvi Syahrini Utami
Sriwijaya Journal of Informatics and Applications Vol 3, No 2 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i2.46

Abstract

Cat is one of the most popular pets. There are many cat breeds with unique characteristic and treatment for each breed. A cat owner can have more than one cat, either the same breed or different breeds.  But not all cat owners know the breeds of their cats. Computers can be trained to recognized cat breeds, but there are many challenges for computers because it limited by how much they have been trained and programmed. In recent years, a lot of research about image classification has been done before and got various result, but most of the data used in previous research were single object images. Therefore, this study of cat breeds classification would be conducted with Convolutional Neural Network (CNN) in the Multi-Object images. This method was chosen because it had good classification results in the previous studies. This study used 5 breeds of cats with every breed having 200-3200 images for training. The test results were measured using confusion matrix, obtaining the precision, recall, f1 score and accuracy of 100% on multi-object images with 2 objects and 3 objects. On images with 4 objects achieved the precision, recall, f1 score and accuracy value of 89%, 87%, 87% and 95%. While the value of precision, recall, f1 score and accuracy on images with 5 objects get 87%, 86%, 86% and 94%, respectively.
Fuzzy Time Series Optimization using Particle Swarm Optimization for Forecasting the Number of Fresh Fruit Bunches (FBB) of Palm Oil Aisyah Filza Aliyah; Alvi Syahrini Utami; Nabila Rizky Oktadini
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.v2i2.21

Abstract

Palm oil is a reliable vegetable oil producer because the oil produced has advantages than oils from other plants. The amount of Fresh Fruit Bunches (FFB) raw material from Palm oil has a significant impact on the palm oil production process. Therefore, we need a method to forecasting the amount of palm oil (FFB). One of the suitable forecasting methods is fuzzy time series (FTS). However, FTS still has shortcomings such as innacurate determination of the interval length. For this reason, we need to optimize FTS interval to get optimal forecasting. This research implements Particle Swarm Optimization as the optimization method, FTS Chen-Hsu as the forecast method, and Mean Absolute Percentage Error (MAPE) as the measurement of error. The optimization result using PSO produce an error value of 2.0262% smaller than FTS 3.7108%.
NL2SQL For Chatbot with Semantic Parsing Using Rule-Based Methods Kurniawan, Adi; Abdiansah, Abdiansah; Utami, Alvi Syahrini
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.v5i1.66

Abstract

Structured Query Language (SQL) is a command language that allows users to access database information. Ordinary people generally donot know how to make queries with SQL to a database. The chatbot is acomputer program developed to interact with its users via text or voice. In this study, chatbots were developed to help and facilitate users intheNatural Language to Structured Query Language (NL2SQL) process tosearch for information in an Academic Information Systemdatabasewith semantic parsing using a rule-based method that accepts input inthe form of interrogative sentences or order. In the Natural Language toStructured Query Language (NL2SQL) process several problems arise, namely input problems with unique parameters for the knowledge base, and slow searching or translation processes, which make Natural Language to Structured Query Language (NL2SQL) inef icient, problems This problem will be solved using a semantic parsingapproach using a rule-based method that is proven to be ef icient insolving issues such as the Natural Language to Structured QueryLanguage (NL2SQL) process. The results showed that the semanticparsing approach using the rule-based method succeeded in obtainingan accuracy rate of 96.72% using 122 test data in the formof questionsentences or command data about the Academic Information Systemof the Department of Informatics Engineering, Sriwijaya University inIndonesian, and an average execution time of 50.68 milliseconds. seconds or 0.05 seconds.
Comparison Of Dempster Shafer AND Certainty Factor Methods In Expert System For Early Diagnosis Of Stroke Disease Arsalan, Osvari; Febrivia, Pretty Fujianti; Utami, Alvi Syahrini; Rodiah, Desty
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.v5i1.79

Abstract

Stroke is one of endangering disease if not treated properly and could lean to death. Most people unwilling to check their health because of high cost, lack of medical service, medical staff of neurologist and their limited working time. Therefore, we need an expert system that can help in early diagnosis of stroke. The Dempster Shafer and Certainty Factor methods are expert systems methods used in many cases to support uncertainty from the expert. The aim of this study is to compare two methods to determine the best method in the expert system for diagnosing stroke, by calculating symptoms so as to produce CF values in the Certainty Factor method and density values in the Dempster Shafer method. The data used in the study to diagnose stroke consisted of data on eighteen disease symptoms and two types of stroke identified. Based on the results of testing on 105 test data, the accuracy value of the expert system for diagnosing stroke using the Dempster Shafer method is 95.2% and the accuracy value of the expert system for diagnosing stroke with the Certainty factor method is 98.1%.
Bully Comments Classification on TikTok Using Support Vector Machine and Chi-Square Feature Selection Putri, Amelia; Abdiansyah, Abdiansyah; Utami, Alvi Syahrini
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.v5i1.71

Abstract

TikTok has been named the world’s most popular social media platform. The high level of TikTok use makes it easier for an irresponsible user to do unethical things such as spreading hateful comments on someone’s account. TikTok developers can prevent bullying by using policies such as word detection and filtering features that indicate comments fall under the category of bullying or non-bullying comments. Therefore, we conducted this study to classify bullying comments using Machine Learning methods for convenience purposes on TikTok usage, a method that we used in this research is the SVM method to classify the data and Chi-Square as the feature selection. Tests were carried out using the Linear, Polynomial, and RBF kernel functions with the C parameter, namely 0,1, 1, and 10 for each kernel. The results of this research show that the Support Vector Machine method with Chi-Square Feature Selection has a better performance.  This was proven by the increased accuracy in RBF kernel C=0,1 which was 0,20
Fisheries Harvest Prediction using Genetic Algorithm Optimized of Gated Recurrent Unit Herman, Adelwin; Utami, Alvi Syahrini; Darmawahyuni, Annisa
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.v5i2.109

Abstract

Indonesia is a maritime country with most of the population living near water areas. Water products are a common commodity often consumed cheaply, and food is therefore one of the primary human needs. Fishery harvest predictions are needed to control prices, prepare seeds, and ensure stable sales and consumption. The reason for choosing GRU for this prediction is that classical methods, commonly used in econometrics or time series analysis, were previously prevalent. GRU requires fewer operations than LSTM. Instead of training with an optimization algorithm relying on backpropagation and gradients, metaheuristic optimization in the form of a GA is used. GA does not require gradient information and is expected to avoid local optima. The total average MSE obtained is 9.55%.
Diagnosing Disease Of Betta Fish Using Fuzzy Logic Sugeno And Forward Chaining Method Pratama, M Wahyu; Utami, Alvi Syahrini; Kurniati, Rizki
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.v5i2.87

Abstract

Betta fish are very popular with people of all ages who make betta fish as small and medium businesses who consider betta fish as a business, but betta fish maintenance is quite the opposite. The ease of breeding betta fish and ignorance in caring for good fish can cause death in betta fish caused by various diseases. Therefore, an expert system for diagnosing disease in betta fish was created. In this study the Forward Chaining method was used to diagnose fish disease, while the Fuzzy Sugeno method determined the severity of the disease. The accuracy generated by the system based on tests carried out using 100 data was 93%.
Co-Authors Abdiansah Abdiansah, Abdiansah Abdiansyah Abdiansyah, Abdiansyah Adi Kurniawan Ahmad Fali Oklilas Aisyah Filza Aliyah Ari Wedhasmara Armenia Yuhafiz Arya Yunanta Darmawahyuni, Annisa Desty Rodiah Dian Palupi Rini Dian Palupi Rini Endang Lestari Ruskan Febrivia, Pretty Fujianti Harisatul Aulia Herman, Adelwin Kanda Januar Miraswan Mastura Diana Marieska Mudinillah, Adam Muhammad Fachrurrozi Muhammad Qurhanul Rizqie Naura Qatrunnada Novi Yusliani Oktadini, Nabila Rizky Oktaviani, Elvira Osvari Arsalan Pratama, M Wahyu Putri, Amelia Rizki Kurniati Wiwik Anum Puspita YUNITA