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All Journal Sistemasi: Jurnal Sistem Informasi Indonesian Journal of Artificial Intelligence and Data Mining JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknik Informatika UNIKA Santo Thomas J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Data Mining dan Sistem Informasi Journal of Soft Computing Exploration J-SAKTI (Jurnal Sains Komputer dan Informatika) Parta: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Multidisiplin Madani (MUDIMA) Applied Science and Technology Research Journal
Yulita, Winda
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Analisis Hubungan dan Prediksi Depresi Mahasiswa Berdasarkan Faktor Akademik dan Gender Verdiana, Miranti; Dwi Nugroho, Eko; Anggraini, Leslie; Bagaskara, Radhinka; Yulita, Winda; Afriansyah, Aidil; Habib Algifari, Muhammad
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

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

Abstract

This study aims to analyze the level of depression among university students by examining gender and several academic indicators. The dataset includes responses from 27,901 students across various regions, with variables covering age, gender, academic pressure, study satisfaction, work/study hours, CGPA, and depression status. The analytical methods applied in this study include the chi-square test to eval_uate the association between gender and depression status, point-biserial correlation to examine relationships between numeric variables and depression, and logistic regression to develop a prediction model. The chi-square test results revealed no significant relationship between gender and depression (p = 0.774), indicating that depression affects both genders. In contrast, academic pressure exhibited the strongest correlation with depression status (r = 0.47), followed by work/study hours (r = 0.209) and study satisfaction (r = -0.168). The Logistic Regression model constructed using the four most relevant variables demonstrated satisfactory performance, achieving 75.5% accuracy and 82.1% recall in identifying students experiencing depression. These findings highlight the critical role of academic-related factors—particularly academic pressure—in influencing students’ mental health. Therefore, targeted academic support strategies are essential to mitigate depression risks in higher education environments. Keywords— Student Depression, Academic Pressure, Gender, Logistic Regression, Mental Health Prediction
Ideal Temperature Classification of Meeting Rooms Using You Only Look Once Architecture Version 8 and Multilayer Perceptron Based on Human Density Image Data Ridwan, Naufal Taufiq; Yulita, Winda; Kesuma, Rahman Indra; Ramadhani, Uri Arta; Bagaskara, Radhinka
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.34230

Abstract

Indonesia, located along the equator, experiences a tropical climate that results in high indoor temperatures. Elevated temperatures can affect health, making air conditioning (AC) necessary to regulate indoor environments. However, improper use of AC systems, such as leaving them on even when a room is unoccupied, can lead to significant energy waste. This research focuses on the efficient use of AC systems through the integration of sensors and cameras, combining two distinct technologies. The first technology is object detection using You Only Look Once (YOLOv8), which was chosen for its superior performance in terms of speed, accuracy, and computational efficiency. The second is the classification of optimal AC temperatures using the Multilayer Perceptron (MLP) algorithm, selected for its high performance in accuracy, sensitivity, and speed. In addition, the study takes into account human density in the room to optimize temperature regulation. The integration of object detection and temperature classification technologies enables the system to operate in real time and automatically adjust temperature settings based on dynamic room conditions. The research successfully implemented YOLOv8 for object detection and Multilayer Perceptron for optimal room temperature classification. Test results showed precision, recall, and F1-score values of 0.82, 0.92, and 0.86, respectively.
Sentiment Analysis of the Minister of Education and Culture using Vader and RBF, Polynomial, Linier Kernels SVM Based on Binary Particle Swarm Optimization Sinaga, Rutlima; Ashari, Ilham Firman; Yulita, Winda
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.2186

Abstract

Comments from social media can be analyzed further. Social media is used to interact from one person to another, as well as with the government. This Issue Was Raised Because Of Debate And Public Opinion From The Community, Institutions And Ngos Regarding Ministerial Regulation No. 30 Of 2021 Concerning Prevention And Handling Of Sexual Violence. In The Higher Education Environment, Therefore In This Research We Want To Examine What Is The Main Root Of The Problem Using A Methodical Approach Using Natural Language Processing. The pre-processing applied is case folding, tokenization, elimination of stop words, stemming using literature. The model implementing PSO failed to improve accuracy on all kernels. Best performance before applying PSO to twitter dataset using linear kernel. This study conducted sentiment analysis regarding the issuance of ministerial regulation no. 30 of 2021. The data obtained was then preprocessed. The performance measured is accuracy and f1-macro in the model without PSO and accuracy in the model using accuracy. The model to be formed uses linear kernels, RBF and polynomials of order 1 and order 2. Sentence analysis is a field that analyzes sentiment, attitudes and emotions of entities and their attributes in text form. The aim of this research is to compare the performance of the Support Vector Machine classification algorithm without Particle Swarm Optimization feature selection and the performance of the Support Vector Machine classification algorithm using Particle Swarm Optimization feature selection. The data obtained is then pre-processed. The data set was automatically labeled using VADER (Valence Dictionary for Sentiment Reasoning). The kernels that succeeded in increasing accuracy were the RBF kernel and polynomials on the Twitter dataset. Keywords: SVM, Vader, PSO, Sentiment Analysis, Government Policy
Design and Implementation of an IoT-Based Misting Control System for Orchid Plants in the ITERA Botanical Gardens Miranto, Afit; Fil’aini, Raizummi; Ramadhani, Uri Arta; Pertiwi, Kisna; Yulita, Winda; Setiawan, Andika; Mufidah, Zunanik; Astuti, Resti Dwi
Jurnal Multidisiplin Madani Vol. 3 No. 11 (2023): November, 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/mudima.v3i11.7096

Abstract

Orchids are one type of plant that can be cultivated in a greenhouse. The growth of orchid plants is influenced by several factors, including altitude, light intensity, envirnmental temperature, environmental humidity, and water content in the planting medium. If some of these factors do not meet the needs, then the growth of the orchid plant will be disrupted, the roots and flowers produced by the orchid will not be healthy and beautiful. The aim of this research is to create a system that is able to monitor and control the environmental conditions in the place/location where this orchid grows so that it is always in a stable condition. The technology used is misting misting based on an Android application using sensors and IoT to maintain and maintain the condition of orchid plants so that they are always in optimal condition. In the results of the tests carried out, the data obtained were good, namely that this instrument had an accuracy of 80%. The application that has been created is capable of controlling and monitoring the environmental conditions of orchids very well  
Digitalisasi Informasi Sebagai Penunjang Efektivitas Pelayanan Administrasi Koperasi Argo Mulyo Lestari Untoro, Meida Cahyo; Kurniawansyah, Apri; Perdana, Agung Mahadi Putra; Praseptiawan, Mugi; Nugroho, Eko Dwi; Afriansyah, Aidil; Yulita, Winda; Verdiana, Miranti
Parta: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2023)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/parta.v4i2.4588

Abstract

Koperasi memiliki peran penting dalam perekonomian Indonesia. Argo Mulyo Lestari, salah satu koperasi yang mengelola dan menyediakan bibit pohon dan buah-buahan serta melakukan pendistribusian keseluruh wilayah Indonesia. Hasil observasi dengan cara wawancara mendapatkan data tentang proses bisnis yang dilakukan koperasi masih tergolong kuno, dengan cara mencatat pada buku, menyimpan pada excel. Proses bisnis yang tidak diimbangi dengan Teknologi informasi dan komunikasi mengakibatka, terjadi duplikasi data dan akses terbatas bagi seluruh anggota koperasi. Tim pengusul membuat usulan untuk menyelesaikan permasalahan dengan cara Teknologi Tepat Guna Digitalisasi Administrasi Koperasi Argo Mulyo Lestari. Tujuan dari digitalisasi, mempermudah, meningkatkan, dan keterbukaan data dalam melaksanakan proses bisnis. Digitalisasi mencangkup proses bisnis administrasi umum, simpan pinjam, keuangan dan pelaporan keuntungan serta kerugian. Teknologi tepat guna akan dievaluasi dengan menggunakan usability test. Hasil dari pengambdian, koperasi Argo Mulyo Lestari sudah menerapkan digitalisasi teknologi yang transparan, dan bertanggung jawab. Digitalisasi administrasi merupakan langkah yang tepat dalam menghadapi perkembangan teknologi informasi yang semakin canggih.
Analysis Comparison of Depression Levels Based on Gender and Academic Factors of Students Verdiana, Miranti; Nugroho, Eko Dwi; Anggraini, Leslie; Bagaskara, Radhinka; Yulita, Winda; Afriansyah, Aidil; Algifari, Muhammad Habib
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v4i2.7975

Abstract

This study aims to analyze the level of depression among university students by examining gender and several academic indicators. The dataset includes responses from 27,901 students across various regions, with variables covering age, gender, academic pressure, study satisfaction, work/study hours, CGPA, and depression status. The analytical methods applied in this study include the chi-square test to evaluate the association between gender and depression status, point-biserial correlation to examine the relationship between numeric variables and depression, and logistic regression to develop a prediction model. The chi-square test results revealed no significant relationship between gender and depression (p = 0.774), indicating that depression affects both genders. In contrast, academic pressure exhibited the strongest correlation with depression status (r = 0.47), followed by work/study hours (r = 0.209) and study satisfaction (r = -0.168). The Logistic Regression model constructed using the four most relevant variables demonstrated satisfactory performance, achieving 75.5% accuracy and 82.1% recall in identifying students experiencing depression. These findings highlight the critical role of academic-related factors—particularly academic pressure—in influencing students' mental health. Therefore, targeted academic support strategies are essential to mitigate depression risks in higher education environments.
From Speech to Summary: A Pipeline-Based Evaluation of Whisper and Transformer Models for Indonesian Dialogue Summarization Manullang, Martin Clinton Tosima; Yulita, Winda; Kartagama, Fathan Andi; Putra, A. Edwin Krisandika
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11826

Abstract

The rapid increase in online meetings has produced massive amounts of undocumented spoken content, creating a practical need for automatic summarization. For Indonesian, this task is hindered by a dual-faceted resource scarcity and a lack of foundational benchmarks for pipeline components. This paper addresses this gap by creating a new synthetic conversational dataset for Indonesian and conducting two systematic, discrete benchmarks to identify the optimal components for an end-to-end pipeline. First, we evaluated six Whisper ASR model variants (from tiny to turbo) and found a clear, non-obvious winner: the turbo (distil-large-v2) model was not only the most accurate (7.97% WER) but also one of the fastest (1.25s inference), breaking the expected cost-accuracy trade-off. Second, we benchmarked 13 zero-shot summarization models on gold-standard transcripts, which revealed a critical divergence between lexical and semantic performance. Indonesian-specific models excelled at lexical overlap (ROUGE-1: 17.09 for cahya/t5-base...), while the multilingual google/long-t5-tglobal-base model was the clear semantic winner (BERTScore F1: 67.09).
SURVEY UKURAN KESAMAAN SEMANTIC ANTAR KATA Styawati, Styawati; Yulita, Winda; Sarasvananda, Sarasvananda
Jurnal Data Mining dan Sistem Informasi Vol 1, No 1 (2020): Agustus 2020
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v1i1.803

Abstract

Pengukuran kesamaan semantic antara kata telah dikembangkan dalam beberapa tahun terakhir. Pengukuran kesamaan semantic antar kata dikelompokkan menjadi tiga yaitu distance based method, information content based method  dan distance and information content based method. Distance based method adalah cara yang lebih alami untuk mengukur kesamaan semantic antara kata. Pendekatan ini memperkirakan jarak antara node yang sesuai dengan konsep yang dibandingkan. Information content based method menggunakan basis pengetahuan yang sistematis dan statistik informasi kata-kata dari database seperti WordNet, Corpus dan HowNet. Distance and information content based method merupakan penggabungan dari pendekatan berbasis jarak dengan pendekatan berbasis konten informasi.
Analisis Sentimen Terhadap Opini Masyarakat Tentang Vaksin Covid-19 Menggunakan Algoritma Naïve Bayes Classifier Yulita, Winda
Jurnal Data Mining dan Sistem Informasi Vol 2, No 2 (2021): Agustus 2021
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v2i2.1344

Abstract

Wabah virus corona telah membawa langkah-langkah yang belum pernah terjadi sebelumnya, yang memaksa pihak berwenang untuk membuat keputusan terkait dengan penerapan lockdown di beberapa daerah yang dilanda pandemi. Media sosial telah menjadi pendukung penting bagi orang-orang saat melewati masa sulit ini. Pada tanggal 9 November 2020, ketika vaksin pertama dengan tingkat efektif lebih dari 90% telah diumumkan, media sosial telah bereaksi dan orang-orang di seluruh dunia mulai mengekspresikan perasaan mereka terkait dengan vaksinasi. Penelitian ini bertujuan untuk menganalisis pendapat tentang vaksinasi COVID-19 di Indonesia. Analisis dilakukan terhadap data 3780 tweet yang berkaitan vaksinasi dengan menggunakan algoritma Naïve Bayes Classifier. Berdasarkan analisis, dapat diamati bahwa sebagian besar tweet memiliki sikap positif (60,3 %), sementara jumlah tweet yang netral (34,4 %) melebihi jumlah tweet yang menentang (5,4 %). Nilai akurasi yang dihasilkan sebesar 0,93 (93 %).
Co-Authors Afit Miranto Afriansyah, Aidil Algifari, Muhammad Habib Andika Setiawan, Andika Anggraini, Leslie Aprilianda, Mohamad Meazza Astuti, Resti Dwi Atmajaya, Gde KM Bagaskara, Radhinka Bahri, Samsu Ba’its, Alfian Kafilah Drantantiyas, Nike Dwi Grevika Dwi Nugroho, Eko Eko Dwi Nugroho Faisal, Amir Febrianto, Andre Fernandes, Rivaldo Fil’aini, Raizummi Fitrawan, Mhd. Kadar Habib Algifari, Muhammad Harmiansyah Ilham Firman Ashari Kartagama, Fathan Andi Kisna Pertiwi, Kisna Kurniawansyah, Apri Listiani, Amalia Martin Clinton Tosima Manullang, Martin Clinton Tosima Mufidah, Zunanik Perdana, Agung Mahadi Putra Praseptiawan, Mugi Putra, A. Edwin Krisandika Rahman Indra Kesuma Ramadhani, Uri Arta Ridwan, Naufal Taufiq Rinaldi, Daniel Rizkita, Alya Khairunnisa Sarasvananda, Sarasvananda Sinaga, Rutlima Solly Aryza Styawati, Styawati Untoro, Meida Cahyo Verdiana, Miranti