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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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Penerapaan Sistem Informasi Desa Menggunakan OpenSID di Desa Tanjung Dayang Selatan, Kabupaten Ogan Ilir, Sumatera Selatan Abdiansah Abdiansah; Alvi Syahrini Utami; Novi Yusliani; Kanda Januar Miraswan; Ahmad Fali Oklilas
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 6 (2021): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v5i6.5621

Abstract

Sistem Informasi Desa (SID) adalah seperangkat alat dan proses pemanfaatan data dan informasi untuk mendukung pengelolaan sumber daya berbasis komunitas di tingkat desa. SID merupakan bagian dari implementasi Undang-Undang (UU) Desa. UU Desa Pasal 86 UU no. 6 Tahun 2014 tentang Sistem Informasi Pembangunan Desa dan Pembangunan Kawasan Perdesaan. Desa belum dapat mengimplementasikan SID karena kurangnya pemahaman perangkat desa tentang SID. Oleh karena itu, kebutuhan akan pelatihan SID menjadi sangat urgen bagi desa khususnya para perangkat desa. Untuk mengatasi masalah tersebut, Fakultas Ilmu Komputer, Universitas Sriwijaya, melakukan kegiatan pelatihan SID berbasis OpenSID melalui program Pengabdian Kepada Masyarakat (PkM). Hasil dari pelatihan diperoleh bahwa materi yang diberikan cukup dipahami oleh semua peserta pelatihan meskipun mereka belum pernah menggunakan aplikasi yang sejenis. Selain itu, mereka percaya bahwa SID dapat membantu administrasi desa dan meningkatkan layanan informasi desa.
Query Reformulation for Indonesian Question Answering System Using Word Embedding of Word2Vec Alvi Syahrini Utami; Novi Yusliani; Mastura Diana Marieska; Abdiansah Abdiansah
Computer Engineering and Applications Journal Vol 11 No 1 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.716 KB) | DOI: 10.18495/comengapp.v11i1.394

Abstract

Query reformulation is one of the tasks in Information Retrieval (IR), which automatically creates new queries based on previous queries. The main challenge of query reformulation is to create a new query whose meaning or context is similar to the old query. Query reformulation can improve the search for relevant documents for Open-domain Question Answering (OpenQA). The more queries are given to the search system, and the more documents will be generated. We propose a Word Predicted and Substituted (WPS) method for query reformulation using a word embedding word2vec. We tested this method on the Indonesian Question Answering System (IQAS). The test results obtained an E-1 value of 81% and an E-2 value of 274%. These results prove that the query reformulation method with WPS and word-embedding can improve the search for potential IQAS answers.
Prediksi Cuaca di Kota Palembang Berbasis Supervised Learning Menggunakan Algoritma K-Nearest Neighbour Alvi Syahrini Utami; Dian Palupi Rini; Endang Lestari
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 13 No 1 (2021): JUPITER Vol. 13 No. 1 April 2021
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

AbstrakPermasalahan cuaca yang dipengaruhi banyak faktor alam menyebabkan kondisi cuaca yang berubah - ubah sehingga kadang sulit diprediksi. Prediksi cuaca yang tepat diperlukan agar masyarakat dan para pengambil kebijakan dapat melakukan antisipasi terhadap hal ini. Banyaknya factor yang mempengaruhi cuaca menyebabkan kesulitan dalam mengklasifikasikan cuaca pada hari tertentu. Locality Sensitive Hashing (LSH) bekerja pada data pelatihan dengan memberikan nilai hash pada tiap vektor yang berisi nilai yang merepresentasikan faktor – faktor yang mempengaruhi cuaca dan melakukan pengklasifikasian cuaca. Untuk selanjutnya algoritma k-Nearest Neighbour (k-NN) yang akan menghitung prediksi terhadap faktor – faktor yang mempengaruhi cuaca pada suatu hari tertentu. Berdasarkan pengujian yang dilakukan, metode k-NN yang dihybrid dengan LSH dapat memprediksi nilai factor – factor yang mempengaruhi cuaca dengan cukup baik dengan nilai Mean Square Error (MSE) sebesar 0,301.  Kata kunci—k-Nearest Neighbour (k-NN), prediksi cuaca, Locality Sensitive Hasihing (LSH) AbstractWeather is influenced by many natural factors causing it to change frequently at any time so that it is sometimes difficult to predict. An accuratet weather prediction is needed so that people and policy makers can anticipate this problem. Many factors that influence the weather cause difficulty in classifying the weather on a particular day. Locality Sensitive Hashing (LSH) works on training data by assigning hash values to a vectors that contain values that represent factors that affect weather and perform weather classification. Furthermore, the k-Nearest Neighbor (k-NN) algorithm will calculate the predictions of the factors that affect the weather on a certain day. Based on the tests carried out, k-NN and LSH in weather prediction has Mean Square Error (MSE) 0,301. Keywords— k-Nearest Neighbou r(k-NN), weather forecasting, Locality Sensitive Hasihing (LSH
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

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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.
Simulasi Antrian Satu Channel Dengan Tipe Kedatangan Berkelompok Alvi Syahrini Utami
Generic Vol 4 No 1 (2009): Vol 4, No 1 (2009)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Masalah antrian tidak hanya terjadi dalam kegiatan sehari – hari namun juga dapat terjadi pada suatu sistem komputer. Antrian yang akan dibahas memiliki sebuah server dengan satu garis antrian yang melayani unit dalam antrian satu per satu dengan tipe kedatangan berkelompok. Pola kedatangan pada antrian ini berdistribusi Poisson dan pola pelayanan berdistribusi Eksponensial dengan disiplin antrian FIFO ( First In First Out ). Untuk mengamati perilaku sistem antrian digunakan simulasi yang akan dijalankan dengan memberikan input yang berbeda-beda dan akan mempengaruhi output sistem. Dari hasil simulasi diharapkan dapat diketahui karakteristik sistem antrian terutama probabilitas kesibukan server sehingga dapat dijadikan landasan untuk pengambilan keputusan terhadap sistem antrian yang diamati.
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.
Analisis Sentimen di Twitter Menggunakan Algoritma Artificial Neural Network Novi Yusliani; Armenia Yuhafiz; Mastura Diana Marieska; Alvi Syahrini Utami
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 15 No 1d (2023): Jupiter Edisi April 2023
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./6603/15.jupiter.2023.04

Abstract

Along with the development of social media, the amount of data in the form of opinions is increasing. The opinions in social media can be used to find out the assessments of social media users regarding something, one of which is the assessment of a candidate in politics. In general, the opinions in social media can be classified into two categories, namely positive and negative. Sentiment analysis is one of the research topics in the field of Natural Language Processing which aims to classify opinions into one of these categories. The opinions in social media that are often used as research objects are the opinions of Twitter users. This study uses an Artificial Neural Network (ANN) algorithm to be implemented in sentiment analysis system. The dataset used in this study is 1088 tweets consisting of 700 tweets labeled positive and 388 tweets labeled negative. The test results show that the best performance is produced when the data is divided into 80% for training and 20% for testing. The resulting percentages for each performance parameter used are accuracy is 61.3%, recall is 67.9%, precision is 75.1%, and f1-score is 71.3% using 0.01 for learning rate and 150 for epoch.
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.
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