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All Journal JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi SINTECH (Science and Information Technology) Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMATICS Journal on Education JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika Global Prosiding Seminar Nasional Multidisiplin Jurnal Sistem informasi dan informatika (SIMIKA) At-Thullab : Jurnal Pendidikan Guru Madrasah Ibtidaiyah Jurnal Inovasi Penelitian Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian kepada Masyarakat Journal of Education Research Indonesian Community Journal Edu Research : Jurnal Penelitian Pendidikan Inspirasi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Media Bina Ilmiah Journal of Research Applications in Community Services AL-UMRON: Jurnal Pengabdian kepada Masyarakat E-Amal: Jurnal Pengabdian Kepada Masyarakat Almantiq Jurnal Teras Pengabdian Masyarakat NAWASENA BHAKTI
Ita Aristia Sa'ida
Universitas Nahdlatul Ulama Sunan Giri
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Optimizing Gated Recurrent Unit (GRU) for Gold Price Prediction: Hyperparameter Tuning and Model Evaluation on Historical XAU/USD Data Faqih, Abdul; Vikri, Muhammad Jauhar; Sa’ida, Ita Aristia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2352

Abstract

This study investigates the use of a Gated Recurrent Unit (GRU) model with a four-layer architecture for daily gold price closing prediction, motivated by the model's ability to effectively capture temporal dependencies in time series data. Gold price forecasting is highly challenging due to its volatility and external factors, making it an important area of research for investors and financial analysts. By systematically optimizing hyperparameters through 72 combinations of epochs, batch size, GRU layer units, and dropout rates, the study identifies the optimal configuration (100 epochs, batch size of 16, 256 units, dropout rate 0.1) based on MSE performance on validation data. The best model achieved MAE of 25.76, MSE of 954.97, and RMSE of 30.90, after inverse transformation on test data. These results highlight the potential of the GRU model in accurately forecasting gold prices, with implications for financial decision-making . However, the prediction error suggests that further improvements could be made by incorporating external factors or exploring advanced model architectures.
Sistem Kamera Cerdas Pendeteksi Kendaraan Salip Kiri untuk Mengurangi Laka Lantas Berbasis Pembelajaran Mesin agustian, rifan; Dirgantoro, Guruh Putro Dirgantoro; Sa’ida, Ita Aristia Sa’ida
Multidisciplinary Applications of Quantum Information Science (Al-Mantiq) Vol. 5 No. 2 (2025): Multidisciplinary Applications of Quantum Information Science (Al-Mantiq)
Publisher : Al-Mantiq

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/almantiq.v5i2.5256

Abstract

Sistem ini dirancang untuk mendeteksi pelanggaran lalu lintas berupa salip kiri menggunakan kamera dan algoritma YOLOv5. Sistem memanfaatkan ESP32 untuk mengontrol floodlight sebagai peringatan serta mengirim notifikasi otomatis ke Telegram. Pengujian menunjukkan deteksi real-time dapat dilakukan dengan tingkat akurasi tinggi dan respons cepat. Hasil implementasi juga menunjukkan sistem ini efisien, murah, serta mudah diadopsi di wilayah rawan kecelakaan. Studi ini diharapkan dapat menjadi solusi teknologi dalam mendukung keselamatan jalan.
PENGUATAN KAPASITAS USAHA MIKRO MELALUI LITERASI DIGITAL MARKETING DAN KEWIRAUSAHAAN DI DESA NGEPER saida, ita aristia saida; Tawakkal, M. Iqbal
Nawasena Bhakti Vol. 1 No. 1 (2025): NAWASENA BHAKTI: Jurnal Pengabdian Masyarakat
Publisher : Badan Usaha Milik Desa Berkaho Pungpungan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64084/nawasenabhakti.v1i1.1

Abstract

This community service program aims to enhance the capacity of Micro, Small, and Medium Enterprises (MSMEs) in Ngeper Village, Padangan District, Bojonegoro Regency through entrepreneurship and digital marketing literacy training. The program was implemented in several stages: preparation, socialization, training, and evaluation. The two-day training employed a participatory and practical approach, covering topics such as basic entrepreneurship, digital content creation, utilization of social media and marketplaces, and setting up Google Business Profiles. The results show that the training was highly relevant to the participants’ needs, with a satisfaction rate of 92%. The program positively impacted participants' understanding and skills in digital product marketing. As a follow-up, a communication group was formed, and further training and the establishment of a digital MSME community in Ngeper Village are planned.
Pelatihan pembuatan Briket Arang Untuk Meningkatkan Kemandirian Ekonomi Bagi Ibu PKK dan UMKM Desa Karangdayu saida, ita aristia saida
Nawasena Bhakti Vol. 1 No. 2 (2025): NAWASENA BHAKTI : Jurnal Pengabdian Masyarakat
Publisher : Badan Usaha Milik Desa Berkaho Pungpungan

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

Abstract

Karangdayu Village has abundant potential in organic waste such as coconut shells, sawdust, and rice husks, but these resources have not been optimally utilized by the local community. The main issues faced include the low level of waste processing skills and limited access to relevant knowledge and training, particularly among PKK women's groups and micro, small, and medium enterprises (MSMEs). The objective of this community service activity is to improve the community’s skills in processing waste into economically valuable products, specifically through charcoal briquette production training. The service methods include stages such as socialization, hands-on training, evaluation during and after the training, and sustainability strategies through the formation of small business groups. The results show that participants developed a good understanding of briquette production techniques and were able to practice the process independently. Moreover, the formation of business groups managed by PKK and MSMEs marks an initial step in creating enterprises based on local potential. In conclusion, this training not only improved the community's technical skills but also opened new opportunities for environmentally friendly and sustainable businesses.
Analisis Sentimen Ulasan Aplikasi DANA di Google Play Store: Penerapan Support Vector Machine dan Synthetic Minority Over-sampling Technique Fajar Nawulansih, Dewi; Ceisa Santi, Nirma; Aristia Sa’ida, Ita
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1053

Abstract

Analisis sentimen ulasan pengguna aplikasi DANA di Google Play Store menggunakan algoritma Support Vector Machine (SVM). Ketidakseimbangan data ditangani dengan Synthetic Minority Over-sampling Technique (SMOTE), dan optimasi parameter dilakukan melalui GridSearchCV. Sebanyak 1.000 ulasan terbaru dianalisis setelah pre-processing dan transformasi TF-IDF. Model SVM dengan kernel linear menghasilkan akurasi tertinggi sebesar 90%, meningkat dari 84% sebelum penerapan SMOTE dan tuning. Uji paired t-test terhadap hasil 10-fold cross-validation menunjukkan peningkatan yang signifikan secara statistik (p-value < 0,05). Recall kelas negatif meningkat dari 63% menjadi 82%, sementara recall positif mencapai 94%. Word cloud menunjukkan kata “dana” paling sering muncul pada ulasan positif dan “aplikasi” pada ulasan negatif. Kombinasi metode ini meningkatkan performa klasifikasi sentimen terhadap ulasan aplikasi DANA secara signifikan.
IMPLEMENTASI ALGORITMA MULTIPLE LINEAR REGRESSION DALAM MENGESTIMASI HASIL PANEN TANAMAN TEMBAKAU wulan, Diah nawang; Barata, Mula Agung; Sa'ida, Ita Aristia
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3662

Abstract

Penilitian ini bertujuan untuk menaksir panen tembakau petani di Desa Balongrejo menggunakan algoritma regresi linier khusus. Data yang digunakan terdiri dari empat variabel dasar: jumlah bit, jumlah pembelian, jumlah transaksi, dan jumlah jam. Analisis dilakukan secara manual dan dengan bantuan alat statistik. Hasil analisis data menunjukkan bahwa model regresi dapat menjelaskan 95,5% varians dalam data. Selain itu, uji F menunjukkan semua variabel memiliki pengaruh yang signifikan secara bersamaan, sedangkan uji t mengidentifikasi tiga variabel yang memiliki pengaruh signifikan secara terpisah.
REAL-TIME TOMATO QUALITY DETECTION SYSTEM USING YOU ONLY LOOK ONCE (YOLOv7) ALGORITHM: Sistem Deteksi Mutu Tomat Secara Real-time Menggunakan Algoritma You Only Look Once (YOLOv7) Muarofah, Isna Ayu; Vikri, Muhammad Jauhar; Sa'ida, Ita Aristia
ULTIMATICS Vol 15 No 2 (2023): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v15i2.3337

Abstract

Real-time object detection is a crucial aspect of computer vision. With the increasing prominence of the big data field, it has become easier to gather data from various sources. Over the past few decades, computer vision inspection systems have become essential tools in agricultural operations, and their usage has seen a significant rise. Computer vision automation-based technology in agriculture is increasingly being employed to enhance productivity and efficiency. Tomato is a widely utilized crop commodity, finding applications in food, cosmetics, and pharmaceuticals. Consequently, tomato farming continues to evolve and has become one of the nation's export commodities. YOLO is an algorithm capable of real-time object detection and recognition. In this study, the YOLOv7-tiny architecture, which has lower computational overhead, was utilized. For quality detection of tomatoes, they were categorized into three classes: ripe, unripe, and defective. The trained model yielded a recall score of 0.97, precision of 1.0, a PR-curve of 0.838, and an F1-score of 0.81, indicating that the model learned effectively. The research achieved an accuracy of 90.6% on original images with an average IoU of 0.90 and a detection time of 2.7 seconds. In images with added light disturbance, the average accuracy was 91.2%. Images with reduced light yielded an average accuracy of 92%, while images with blur disturbance had an average accuracy of 78.2%. In real-time testing, ripe tomatoes were detected up to a maximum distance of 90cm, unripe tomatoes at 90cm, and defective tomatoes at 70cm.
Pengembangan Model Pembelajaran Citizen Journalism untuk Meningkatkan Civic Skills dan Civic Empathy pada Mahasiswa: Citizen Journalism Sa'ida, Ita Aristia; Dirgantoro, Guruh Putro
Journal of Education Research Vol. 5 No. 3 (2024)
Publisher : Perkumpulan Pengelola Jurnal PAUD Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37985/jer.v5i3.1269

Abstract

Pengembangan warga negara yang demokratis sesuai dengan karakteristik warga negara abad ke dua puluh satu yang menekankan pada dimensi pendidikan, sosial, politik, sosial budaya, dan ekonomi sehingga diperlukan materi dan metode pembelajaran yang tepat. Disamping itu digitalisasi juga berpengaruh pada karakter generasi muda yang mudah terbawa arus instan informasi. Berawal dari kondisi seperti ini, perlu adanya  Penelitian tentang pengembangan model pembelajaran Citizen Journalism untuk meningkatkan civic skills dan civic empathy pada mahasisiwa, dimana penelitian tersebut bertujuan untuk melakukan investigasi dampak pada penggunaan model pembelajaran citizen journalism kepada mahasiswa untuk mengembangkan civic skills dan civic empathy sebagai upaya penguatan Pendidikan karakter bagi para mahasiswa. Penelitian ini dilaksanakan menggunakan pendekatan kualitatif dengan jenis penelitian Tindakan kelas menggunakan model dari bachman. Informan dalam penelitian ini yaitu mahasiswa Teknik informatika Universitas Nahdlatul Ulama Sunan Giri semester dua yang sedang menempuh mata kuliah Pendidikan kewarganegaraan. Hasil penelitian menunjukkan bahwa para mahasiswa telah memiliki perangkat keteram- pilan dan empati sebagai bagian dari civic competencies yang membentuk karakter dan moralitas publik mereka. Melalui model pembelajaran citizen journalism, kedua kompetensi tersebut diperkuat dan tampak lebih jelas dengan pola pembelajaran berbuat dan penyelesaian masalah melalui penyelesaian proyek. Selain itu, hasil analisis menunjukkan bahwa model citizen journalism mampu mengembangkan sikap-sikap demokratis mahasiswa seperti keterbukaan, berpikir kritis, toleran, dan bertanggung jawab.
Algoritma K-Means untuk Mengelompokkan Tingkat Pengangguran Terbuka (TPT) menurut Provinsi di Indonesia Saputra, Agus Bima; Sanjaya, Ucta Pradema; Sa’ida, Ita Aristia
Jurnal Ilmiah Informatika Global Vol. 15 No. 2: Agustus 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i2.4359

Abstract

Unemployment is one of the main problems faced by many countries, including Indonesia. The Open Unemployment Rate (OER) is an important indicator used to measure the amount of labor force that is not absorbed in the labor market. This research aims to Cluster the provinces in Indonesia based on unemployment rate and school enrollment rate, so as to provide a clearer picture of the distribution of unemployment in different regions: The study identified three main Clusters: Cluster 1: Provinces with high unemployment rates. Cluster 2: Provinces with a medium unemployment rate. Cluster 3: Provinces with low unemployment rates. Distribution: The Clustering results show that 13 provinces are included in Cluster 1, 18 provinces in Cluster 2, and 3 provinces in Cluster 3. This study found that the K-Means algorithm is effective in Clustering provinces based on TPT and school enrollment rates. The Clustering results show significant variation between provinces, with some provinces having higher unemployment rates and lower school enrollment than others.This study successfully Clustered Indonesian provinces based on unemployment and school enrollment rates using the K-Means algorithm. The Clustering results provide valuable insights into the distribution of unemployment in Indonesia and can be used as a basis for more effective policy making.
PEMANFAATAN ARTIFICIAL INTELLIGENCE DALAM PEMBELAJARAN DASAR: HASIL PENGABDIAN DI MI PLUS AL-FATIMAH BOJONEGORO Ita Aristia Sa’ida; Guruh Putro Dirgantoro; Dwi Issadari Hastuti; Niken Sukmawati; Elsa Azia Ulhaq
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 6 (2025): Nopember 2025
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v5i6.11805

Abstract

The rapid development of Artificial Intelligence (AI) offers significant opportunities to enhance the quality of learning, particularly at the elementary and madrasah levels. However, teachers’limited digital literacy and lack of experience in utilizing AI-based tools often hinder the integration of technology into classroom practices. This community service program aims to strengthen teachers’ competencies in implementing AI to support teaching and learning at MI Plus Al-Fatimah, Bojonegoro. The program was conducted through four stages: socialization, training, classroom implementation with mentoring, and evaluation. Various AI-based teaching products were produced, including digital learning materials, interactive assessments, infographics, and instructional videos. Evaluation results show a substantial improvement in teachers’ digital literacy and confidence, with an average increase of 65% between pre-test and post-test scores. Teachers reported that AI tools improved efficiency in preparing teaching materials, enhanced classroom engagement, and helped diversify instructional strategies. This program demonstrates that AI integration can serve as an effective capacity-building mechanism for teachers, promoting innovative, adaptive, and technology-driven learning environments. The findings suggest that continuous training, infrastructural support, and clear implementation guidelines are necessary to sustain AI adoption in schools and broaden its impact across educational settings.
Co-Authors Abdul Faqih Abidin, Muhammmad Zaenal Afril Efan Pajri Aftha Harianto, Febby Refindha Agus Afriliyanto agustian, rifan Ainu Zuhriyah Amalia Nur Laily APRILLIA, YUSIFA Audytra, Hastie Ayu Mauliana Intahaya Bakti Putra Pamungkas Ceisa Santi, Nirma Devi Dwi Nuraliza Diah Anggraini, Diah Diah Fitriani Dirgantoro, Guruh Putro Dirgantoro, Guruh Putro Dirgantoro Dwi Issadari Hastuti Eka Asfira Eli Widyawati, Reza Elsa Azia Ulhaq Fajar Nawulansih, Dewi Guruh Putro Dirgantoro Jauhar Vikri, Muhammad M. Iqbal Tawakkal Mochamad Nizar Palefi Ma’ady Muarofah, Isna Ayu Muhammad Adib Asrori Muhammad Zaenal Abidin Mula Agung Barata Nawafila Februyani Niken Sukmawati Nurul Huda Sahri Sahri Sahri Sahri Sanjaya, Ucta Pradema Santi, Nirma Ceisa Saputra, Agus Bima Sholihah, Nurul Maratus Siti Cholifah SITI CHOLIFAH Siti Shoimah Sunu Wahyudhi Taufiq Hidayat Tawakkal, M. Iqbal Wulan, Diah Nawang Wulandari, Devi Ayu Zakki Alawi Zeti Novitasari