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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Nuansa Informatika Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal Journal of Information Technology and Computer Science (JOINTECS) Bina Insani ICT Journal Techno Nusa Mandiri : Journal of Computing and Information Technology IKRA-ITH Informatika : Jurnal Komputer dan Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Informatika Universitas Pamulang Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science STRING (Satuan Tulisan Riset dan Inovasi Teknologi) JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Pendidikan dan Konseling Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Journal Sensi: Strategic of Education in Information System Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS BERNAS: Jurnal Pengabdian Kepada Masyarakat Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta Jurnal Sains Informatika Terapan (JSIT) Journal of Artificial Intelligence and Digital Business Innovative: Journal Of Social Science Research Jurnal INFOTEL
Yuyun Umaidah
Departemen Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Singaperbangsa Karawang, Karawang, 41361
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ANALISIS SENTIMEN MASYARAKAT TERHADAP PROGRAM MAKAN SIANG GRATIS PADA MEDIA SOSIAL X MENGGUNAKAN ALGORITMA NAÏVE BAYES Altolyto Sitanggang; Yuyun Umaidah; Riza Ibnu Adam
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4902

Abstract

Dalam era digital, media sosial seperti X, Facebook, dan Instagram telah menjadi bagian penting dari kehidupan modern, memungkinkan individu untuk berbagi pandangan dan opini dengan cepat. Salah satu topik hangat di X adalah program makan siang gratis dari pasangan calon presiden nomor urut 02, yang bertujuan meningkatkan gizi anak dan ibu hamil, mencakup 82,9 juta orang. Program ini memicu beragam tanggapan masyarakat. Penelitian ini bertujuan menganalisis sentimen masyarakat terhadap program tersebut menggunakan algoritma Naïve Bayes dan metode Knowledge Discovery in Database (KDD). Data dikumpulkan melalui crawling pada media sosial X, menghasilkan 2.211 tweet yang kemudian diseleksi dan diberi label sentimen positif dan negatif. Algoritma Naïve Bayes diuji dengan tiga skenario pembagian data training dan testing, dan dievaluasi menggunakan confusion matrix. Hasil evaluasi menunjukkan model mencapai hasil terbaik pada rasio data 60:40 dengan akurasi 72,2%, presisi 63,2%, recall 66,1%, dan F1-Score 64%. Keywords: Naïve Bayes, KDD, Makan Siang Gratis, X, Sentimen.
Peningkatan Pengetahuan dan Keterampilan Masyarakat Melalui Pembibitan Jamur Merang Di Desa Pasirukeum Kabupaten Karawang Suhaeni Suhaeni; I Ketut Manu Mahatmayana; Indrajit Wicaksana; Yuyun Umaidah
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v4i1.3810

Abstract

Straw mushrooms are one of the food ingredients that contain lots of nutrients and are liked by the community. One of the villages that became the center of straw mushrooms in Karawang Regency is Pasirukeum Village. The mushroom in this village was developed by farmers who are members of the Sri Muncul farmer group. However, over time, the production of straw mushrooms are decreasing year by year. One of the contributing factors is the low quality of mushroom seeds and the lack of knowledge and skills of farmers in conducting nurseries. Based on these problems, this community service activity aims to help the community provide solutions, namely in the form of socialization and training in nursery of straw mushroom . The method used is counseling and training on seed production of straw mushroom. There are four indicators used to measure farmers' knowledge, which are related to seeding and cultivation, mushroom quality, farming risk and income. The results of the implementation show that the knowledge of farmers (partners) about breeding and cultivation has increased by 27%, farmers' knowledge regarding the quality of straw mushrooms has increased by 22.1%, increased knowledge of farmers related to farming risks has increased by 25.6% and increased knowledge of farmers related to income in farming. increased by 23.7%. The average increase in knowledge and skills of farmers as a whole is 24.6%.
Implementasi Computer Vision AI Pada Smart Trash Classification Menggunakan Railway Cloud Deployment Vania Ardelia Zahra; Shafa Aulia Nadhira; Roma Ulina; Yuyun Umaidah; Arip Solehudin
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1151

Abstract

Waste sorting remains a significant challenge in environmental management, mainly because it is still performed manually, which may lead to misclassification of waste types. To address this issue, this study develops a waste classification system based on deep learning by utilizing Computer vision technology. The system is designed to classify five types of waste, namely plastic, glass, metal, paper, and organic waste, based on images uploaded by users. The proposed method includes dataset collection from Kaggle and Google Images, data Preprocessing, model design using the EfficientNet-B0 architecture with a transfer learning approach, and model training until the final model is saved in the .keras format. The system is then implemented into a web-based application using the Flask framework and deployed through Railway cloud computing services to enable online accessibility. The experimental results show that the system is able to classify waste images automatically with good performance and can be accessed flexibly across different devices. This system is expected to improve the efficiency and effectiveness of waste sorting in real-world applications.
Penguatan Kompetensi Guru Dalam Pemanfaatan Teknologi Pembelajaran Dan Edukasi Literasi Digital Bagi Siswa Smp As-Syarifiyah Guna Mewujudkan Ekosistem Pendidikan Cerdas Digital Hannie Hannie; Agung Susilo Yuda; Ratna Mufidah; Iqbal Maulana; Ade Andri Hendriadi; Carudin Carudin; Dadang Yusup; Intan Purnamasari; Rini Mayasari; Taufik Ridwan; Sofi Defiyanti; Billy Ibrahim Hasbi; Yuyun Umaidah; Ahmad Khusaeri
TRIDHARMADIMAS: Jurnal Pengabdian Kepada Masyarakat Jayakarta Vol 6 No 1 (2026): TRIDHARMADIMAS (Juli 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/tridharmadimas.v6i1.2589

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini dilaksanakan oleh Fakultas Ilmu Komputer Universitas Singaperbangsa Karawang di SMP As-Syarifiyah pada bulan November 2025. Latar belakang kegiatan ini berangkat dari permasalahan guru yang masih menghadapi kendala dalam mengintegrasikan teknologi ke dalam proses pembelajaran, serta kerentanan siswa terhadap paparan hoax dan ancaman keamanan data pribadi. Melalui pendekatan partisipatif dan edukatif, kegiatan dilaksanakan dalam dua pilar utama: pelatihan pemanfaatan teknologi pembelajaran bagi guru menggunakan PhET Interactive Simulations dan sosialisasi literasi digital bagi siswa yang mencakup materi keamanan siber dan anti-hoax. Metode yang digunakan meliputi hands-on training, demonstrasi aplikasi, ceramah interaktif, serta evaluasi pre-test dan post-test. Hasil kegiatan menunjukkan bahwa 13 guru telah mendapatkan pelatihan integrasi TIK dalam pembelajaran, sementara 120 siswa berhasil dibekali keterampilan mengidentifikasi hoax dan menerapkan langkah keamanan siber. Evaluasi post-test menunjukkan peningkatan pemahaman pada kedua kelompok sasaran. Meskipun dihadapkan pada kendala infrastruktur TIK dan manajemen waktu, solusi yang diterapkan secara efektif memastikan tercapainya target keluaran. Kegiatan ini berhasil menumbuhkan budaya pembelajaran berbasis teknologi dan memperkuat literasi digital di lingkungan sekolah mitra.
Perbandingan Convolutional Neural Network dan Algoritma Machine Learning Konvensional untuk Klasifikasi Kemiskinan Multidimensional di Indonesia Ruth Tika Sarwanti; Yuyun Umaidah
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp275-281

Abstract

Multidimensional poverty in Indonesia is a complex phenomenon involving various interconnected social, economic, and structural aspects. Conventional approaches to poverty classification often fail to capture non-linear interaction patterns and spatial dependencies inherent in multidimensional socio-economic data. This research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms such as Random Forest and XGBoost in classifying multidimensional poverty in Indonesia. The research method employs a comparative quantitative approach using data from the 2023 National Socio-Economic Survey (Susenas) by BPS, covering 8.000 household observations. The target variable is multidimensional poverty status based on the Multidimensional Poverty Index (MPI) with a 1/3 cutoff. Data was split 70:30 for training and testing, with preprocessing including missing value imputation, one-hot encoding, and Min-Max scaler normalization. The CNN model was designed with a two-convolutional layer architecture, while Random Forest used 200 decision trees and XGBoost with 200 estimators. Research results demonstrate that CNN provides the best performance with 82.4% accuracy, outperforming Random Forest (80.1%) and XGBoost (81.2%). Important variable analysis reveals that housing infrastructure conditions, household head education level, and sanitation access are key factors in determining multidimensional poverty, providing strategic input for formulating more targeted poverty alleviation policies.
Komparasi Algoritma Decision Tree dan Gradient Boosting untuk Prediksi Penerima Bantuan Sosial di Desa Kalijati Barat Fitri Akmaliah; Yuyun Umaidah; Iqbal Maulana
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

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

Abstract

Kemiskinan masih menjadi persoalan sosial utama di Indonesia yang menghambat pemerataan kesejahteraan masyarakat. Program Bantuan Langsung Tunai Dana Desa (BLT-DD) menjadi instrumen pemerintah dalam mengatasi kemiskinan, namun penentuan penerimanya masih dilakukan secara manual dan subjektif sehingga rawan ketidaktepatan sasaran. Penelitian ini membandingkan performa algoritma Decision Tree dan Gradient Boosting dalam memprediksi penerima bantuan sosial di Desa Kalijati Barat, Kabupaten Subang, menggunakan pendekatan Knowledge Discovery in Databases (KDD). Data yang digunakan berupa Buku Induk Penduduk (BIP) Tahun 2019 dan data penerima BLT-DD Tahun 2025. Ketidakseimbangan kelas ekstrem (rasio ±101:1) ditangani menggunakan SMOTE. Model dioptimasi dengan GridSearchCV dan Stratified K-Fold Cross Validation (k=5) pada tiga skenario pembagian data. Hasil menunjukkan kedua algoritma menghasilkan accuracy tinggi (0,91–0,98), namun berdasarkan recall dan F1-score, Decision Tree lebih unggul pada skenario 80:20 (recall=0,3333; F1-score=0,1579), sedangkan Gradient Boosting gagal mendeteksi kelas minoritas pada skenario 90:10. Analisis feature importance menunjukkan tingkat pendidikan kepala keluarga, jenis pekerjaan, dan rasio tanggungan sebagai faktor paling berpengaruh.
Prediksi Titik Kritis Jaringan KRL Jabodetabek Menggunakan Node2Vec dan LightGBM Siti Zulhi Nirma Saidah; Yuyun Umaidah; Betha Nurina Sari
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.6810

Abstract

The Jabodetabek Commuter Rail (KRL) serves millions of passengers daily across a network of 94 active stations and 421 direct service relations. In highly interconnected networks, certain stations are critical, defined as those whose disruption causes the greatest impact on network connectivity and average travel time efficiency. This study develops a predictive model based on Node2Vec and LightGBM under the Knowledge Discovery in Databases (KDD) framework to numerically estimate station criticality levels. Operational schedule data were collected via the Comuline API (30,989 records), processed into a directed weighted graph (94 nodes, 421 edges), and used to compute impact scores through single-node removal simulation as regression labels. A total of 38 features were used: 6 topology metrics and 32 Node2Vec embedding dimensions. 5-Fold Cross-Validation yielded MAE 0.3152%, RMSE 0.8903%, and Spearman correlation 0.7802 (p<0.001) with 95% Bootstrap CI [0.6622; 0.8700]. The model identified 9 of 10 most critical stations (Top-10 recall 90%). Betweenness centrality was the most dominant predictor based on Gain Importance and SHAP analysis. Of 94 stations, 8 are Very Critical, 21 Critical, 20 Moderately Critical, and 45 Relatively Safe. Manggarai and Tanah Abang are the most critical with impact scores of 9.60% and 8.89%.
Pengembangan Asisten Virtual Berbasis NLP untuk Mendukung Kesejahteraan Psikologis Mahasiswa Muhammad Tafazzani Addien; Yuyun Umaidah; Apriade Voutama
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.10114

Abstract

Peningkatan tekanan akademik, sosial, dan personal pada mahasiswa menimbulkan kebutuhan terhadap media pendampingan psikologis awal yang mudah diakses, aman, responsif, dan tidak menghakimi. Penelitian ini bertujuan mengembangkan asisten virtual berbasis Natural Language Processing (NLP) untuk mendukung kesejahteraan psikologis mahasiswa melalui interaksi berbasis teks. Metode penelitian menggunakan Research and Development dengan model Prototype yang meliputi analisis kebutuhan, perancangan arsitektur, implementasi modul NLP, pengujian fungsi, dan evaluasi pengguna. Sistem dirancang menggunakan komponen antarmuka chatbot, preprocessing teks, klasifikasi intent, analisis sentimen, basis pengetahuan kesehatan mental, serta manajemen dialog. Hasil pengembangan menunjukkan bahwa sistem mampu menerima pesan pengguna, mengenali keluhan akademik dan emosional, memproses bahasa informal, memberikan respons suportif, serta menampilkan arahan menuju konselor atau layanan profesional ketika diperlukan. Pengujian black box memperlihatkan bahwa fungsi pengiriman pesan, klasifikasi intent, analisis sentimen, pemilihan respons, penanganan pesan di luar konteks, dan penyajian informasi konseling berjalan sesuai rancangan. Evaluasi pengguna menunjukkan bahwa asisten virtual dinilai bermanfaat sebagai media pendampingan awal karena fleksibel, cepat, dan membantu proses refleksi emosional. Meskipun demikian, sistem masih memerlukan perluasan dataset percakapan, peningkatan pemahaman bahasa slang dan konteks panjang, perlindungan privasi, serta mekanisme deteksi risiko. Asisten virtual tidak menggantikan psikolog atau konselor, tetapi dapat menjadi pintu awal menuju dukungan profesional yang lebih aman dan berkelanjutan.
DETEKSI POTENSI DEPRESI PADA CUITAN X BERBAHASA INDONESIA MENGGUNAKAN INDOBERT DAN SUPPORT VECTOR MACHINE Benedict Ginting; Yuyun Umaidah; Adhi Rizal
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.9902

Abstract

Kesehatan mental, khususnya depresi, merupakan isu kesehatan global yang seringkali sulit dideteksi secara dini. Media sosial X menjadi sarana potensial untuk deteksi dini berdasarkan ekspresi emosional pengguna. Penelitian ini bertujuan mengklasifikasikan cuitan berbahasa Indonesia ke dalam kategori depresi dan non-depresi menggunakan pendekatan gabungan IndoBERT sebagai pengekstraksi fitur dan Support Vector Machine (SVM) sebagai pengklasifikasi. Dataset terdiri dari 1.293 cuitan yang telah divalidasi oleh pakar psikologi klinis. Tahapan analisis meliputi pra-pemrosesan teks, penanganan ketidakseimbangan kelas menggunakan Back-translation dan SMOTE, ekstraksi fitur berdimensi padat dengan IndoBERT, dan klasifikasi menggunakan SVM berkernel linear. Hasil pengujian menunjukkan bahwa arsitektur gabungan ini sangat efektif, dengan tingkat akurasi mencapai 79,34% dan nilai recall untuk kelas depresi menyentuh 80% pada parameter optimal (C=0,01). Tingginya sensitivitas deteksi ini membuktikan bahwa integrasi representasi kontekstual IndoBERT dan ketangguhan margin klasifikasi SVM mampu mendeteksi indikasi gangguan kesehatan mental di ruang siber secara efisien. Hal ini berimplikasi pada kelayakan arsitektur tersebut untuk diimplementasikan sebagai landasan sistem peringatan dini (early warning system).
Implementasi Computer Vision AI Pada Smart Trash Classification Menggunakan Railway Cloud Deployment Vania Ardelia Zahra; Shafa Aulia Nadhira; Roma Ulina; Yuyun Umaidah; Arip Solehudin
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1151

Abstract

Waste sorting remains a significant challenge in environmental management, mainly because it is still performed manually, which may lead to misclassification of waste types. To address this issue, this study develops a waste classification system based on deep learning by utilizing Computer vision technology. The system is designed to classify five types of waste, namely plastic, glass, metal, paper, and organic waste, based on images uploaded by users. The proposed method includes dataset collection from Kaggle and Google Images, data Preprocessing, model design using the EfficientNet-B0 architecture with a transfer learning approach, and model training until the final model is saved in the .keras format. The system is then implemented into a web-based application using the Flask framework and deployed through Railway cloud computing services to enable online accessibility. The experimental results show that the system is able to classify waste images automatically with good performance and can be accessed flexibly across different devices. This system is expected to improve the efficiency and effectiveness of waste sorting in real-world applications.
Co-Authors Ade Andri Hendriadi Adhi Rizal Agung Susilo Yuda Ahmad Khusaeri Ahmad Ridhoi Fajri Altolyto Sitanggang Apriade Voutama Arip Solehudin Asep Jamaludin Benedict Ginting Billy Ibrahim Hasbi Bintang Sifa Amalia Budi Arif Dermawan Carudin Chaerur Rozikin Dadang Yusup Dina Amelia Alzahra Dwi Latifah Rianti Fajar Romadoni Falih Pramataning Dewi Farid Farid Fitri Akmaliah Hannie, Hannie Hendi Permana I Ketut Manu Mahatmayana Indrajit Wicaksana Intan Purnamasari Iqbal Maulana Jaman, Jajam Haerul Kardo Trihandoko Khoirunnisa Hamidah Mayland Trifena Mochammad Riszky Sulaeman Mohammad Rizal Givari Muhammad Nur Yasin Muhammad Tafazzani Addien Nur Maelani Asih Priskila Siwi Aryni Purwantoro Purwantoro Ratna Mufidah Renaldy Permana Sidiq Rianti, Winda Richard Julianno Soeganda Rini Mayasari Risna Alfiani Riza Ibnu Adam Roma Ulina Ruth Tika Sarwanti Shafa Aulia Nadhira Siti Zulhi Nirma Saidah Sofi Defiyanti Suhaeni, Suhaeni Taufik Ridwan Tiara Destiana Titan Nugraha Ultach Enri Ultach Enri Vania Ardelia Zahra