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Pratiwi, Mutiana
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Perancangan Sistem Informasi Laporan Keuangan pada Sekolah Menengah Pertama Arsyah, Ulya Ilhami; Pratiwi, Mutiana; Abulwafa Muhammad
Journal Of Indonesian Social Society (JISS) Vol. 1 No. 1 (2023): JISS - Februari
Publisher : PT. Padang Tekno Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1018.863 KB) | DOI: 10.59435/jiss.v1i1.28

Abstract

Sistem informasi laporan keuangan adalah kegiatan pencatatan laporan keuangan yang diterapkan guna mempermudah dalam proses pengolahan data keuangan. Kegiatan ini bertujuan untuk merancang dan membangun aplikasi pengelolaan transaksi keuangan pada sekolah berbasis web. Sistem informasi ini meliputi sistem pencatatan, pembuatan jurnal umum, buku besar, dan neraca saldo. Pihak bendahara sekolah melakukan proses pencatatan masih menggunakan Ms. Excel. Terkait hal tersebut, dirancang sebuah sistem berbasis akuntansi guna membantu dalam proses pencatatan transaksi keuangan pada sekolah menengah pertama (SMP). Lokasi pengabdian dilakukan pada SMP 24 Padang. Pemanfaatan Sistem Informasi Akuntansi (SIA) ini dapat membantu mengurangi redudansidata, menghasilkan informasi secara cepat, tepat dan akurat serta data laporan keuangan dapat disimpan dengan baik. Metode yang digunakan dalam perancangan aplikasi ini adalah Object Oriented Programming (OOP) dengan menggunakan Unified Modeling Language (UML) dalam analisis perancangan sistem. Hasil kegiatan ini adalah berupa rancangan sistem informasi akuntansi yang dapat mempermudah pencatatan transaksi keuangan yang dilakukan oleh pihak sekolah.
Twitter Sentiment Analysis of Public Space Opinions using SVM and TF-IDF Methods Arsyah, Ulya Ilhami; Pratiwi, Mutiana; Muhammad, Abulwafa
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): 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.v13i1.3594

Abstract

Public space opinion reviews are currently a source of information for interested parties and decision-makers. Twitter is a social media that is a means of expressing themselves for people to express their opinions and criticize the current situation. This becomes information for readers. Information published on Twitter contains elements of commentary on a situation or object Sentiment analysis of public space opinion on Twitter using Machine Learning with the Support Vector Machine (SVM) method with the data weighting process using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Dataset obtained by scraping using the Twitter API as much as 5000 data then labeled where the goal is to get accuracy on positive, negative, or neutral sentiment. The results of research conducted experiments on three Machine Learning algorithms with the extraction function "TF-IDF" obtained an accurate training model with good classification capabilities, especially SVM of 91,6% on data distribution 70: 30; SVM is 92.8% in the case of data distribution of 80: 20; the SVM is 91,8% in the case of 90:10 decomposition data.
Machine Learning on Opinion Mining of Netizen's Hate Speech Pratiwi, Mutiana; Liana Gema, Rima
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): 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.v13i1.3617

Abstract

Netizen comments written in an online news portal through social media platforms, one of which is Instagram, can be used as material in the sentiment analysis process, which can be classified into positive, negative, or neutral sentiments. Sentiment analysis is part of the study of text mining, the science of discovering unknown knowledge by automatically extracting information from large volumes of unstructured text into useful information. The resulting information is in the form of sentiment towards a topic, whether it tends to be positive, negative, or neutral. The classification method used in this research is Support Vector Machine (SVM) and TF-IDF data weighting to classify text. Stages to perform data analysis are pre-processing to clean data, word weighting, labeling data into positive, negative, or neutral classes, and classifying and visualizing data with graphs. Accuracy tests using 70:30 split data showed that the accuracy reached 98%. Tests with 80:20 and 90:10 split data also showed high accuracy of 98% and 99%.
Co-Authors Abulwafa Muhammad Aggy Pramana Gusman Aminuyati Anam, M Khairul Arsyah, Ulya Ilhami Devia Kartika Dinul Akhiyar Fryonanda, Harfebi Gema, Rima Liana Irzal Arief Wisky Kris Hiburan Laoli, Vince Liga Mayola Lusinia, Shary Armonitha Mardhiah, Putri Marfalino, Hari Muhammad, Abulwafa Munawir Munawir Nitami, Dian Rianti, Eva Sarjon Defit Tukino, Tukino