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All Journal ComEngApp : Computer Engineering and Applications Journal Syntax Jurnal Informatika Fountain of Informatics Journal Format : Jurnal Imiah Teknik Informatika Jurnal Informatika Jurnal Ilmiah FIFO Faktor Exacta Jurnal Pengabdian Pada Masyarakat InComTech: Jurnal Telekomunikasi dan Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) IKRA-ITH ABDIMAS JURIKOM (Jurnal Riset Komputer) IJISCS (International Journal Of Information System and Computer Science) MULTINETICS Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Jurnal Ilmu Teknik dan Komputer Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Pengabdian Masyarakat Asia Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia International Journal of Advanced Multidisciplinary Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Systems Engineering and Information Technology IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Pengabdian Pada Masyarakat Media Abdimas Kapas: Kumpulan Artikel Pengabdian Masyarakat Sinergi: Jurnal Pengabdian Kepada Masyarakat IRA Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Nasional Jurnal Pengabdian Masyarakat Nauli CSRID Global Science: Journal of Information Technology and Computer Science Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Saruni Dwiasnati
Universitas Mercu Buana
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Penerapan Gradient Boosting Regression dalam Prediksi Pergerakan Harga Emas Berdasarkan Pendekatan Moving Average of VWAP Abdillah, Reza Wahyu; Dwiasnati, Saruni
InComTech : Jurnal Telekomunikasi dan Komputer Vol 15, No 1 (2025)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v15i1.28304

Abstract

Pergerakan Harga emas dipengaruhi oleh berbagai faktor ekonomi, inflasi, penawaran dan permintaan, serta kebijakan moneter, yang membuat prediksi Harga emas menjadi penting bagi investor. Penelitian ini bertujuan untuk mengembangkan model prediksi Harga emas menggunakan pendekatan Moving Average of VWAP dan Algoritma Gradient Boosting Regression. Data diambil dari situs www.investing.com, mencakup periode 14 Januari 2016 hingga 12 April 2024. Metode penelitian meliputi pembersihan data. Penskalaan dengan StandardScaler, dan pembagian data menjadi set pelatihan dan pengujian, Moving Average of VWAP digunakan untuk menganalisis Harga berdasarkan volume perdagangan, sementara Algoritma Gradient Boosting Regression digunakan untuk klasifikasi dan prediksi Harga actual dan prediksi. Hasil penelitian menunjukan Tingkat akurasi yang sangat tinggi dengan R-Squared (R2) mencapai 0.99 dan evaluasi kinerja model menunjukan MAE sebesar 6.2955, MSE sebesar 78.0802, RMSE sebesar 8.8317. hasil ini menunjukan bahwa model prediksi yang dihasilkan dapat menjadi alat yang efektif bagi investor dalam pengambilan Keputusan investasi emas yang lebih informasional dan strategis.
Quantitative Analysis of Training Completion Using Multivariate Linear Regression Devianto, Yudo; Dwiasnati, Saruni; Gunawan, Wawan; Sumarto, Marco Alfan; Saputra, Dony Ramadhan
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8851

Abstract

The urgency of this research stems from the strategic need to monitor and evaluate the achievements of digital training implemented by various academies under government coordination, including VSGA, FGA, DEA, TA, and GTA. In the context of the national digital transformation program, the availability of an analytical model that can predict the success of participants in completing training is critically crucial to support the achievement of the Ministry’s Key Performance Indicators (KPIs). The purpose of this study is to develop a predictive model based on multivariate linear regression that combines two main variables, the percentage of participants accepted and the percentage of participants who participate in onboarding, to project the level of training completion. This model is expected to provide a quantitative and objective assessment of the effectiveness of digital training implementation in each academy. The targeted outputs of this study include the development of a predictive model with performance validation through the calculation of R², which yielded a value of 0.9448, as well as the provision of technical reports and data-driven recommendations for enhancing digital training governance. The Technology Readiness Level (TKT) of this study is at TKT 3, and there is evidence of conceptual validation of the predictive model based on real data collected from the implementation of the training. This stage marks the readiness of the research to continue developing the system model and implementing it on the training evaluation platform in the next stage.
Pemodelan Wilayah Titik Api Kebakaran Hutan Menggunakan Deep Learning Dwiasnati, Saruni; Devianto, Yudo; Arif, Sutan Mohammad; Avrizal, Reza
Jurnal Ilmiah FIFO Vol 16, No 1 (2024)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2024.v16i1.001

Abstract

Indonesia merupakan negara tropis yang mengalami kebakaran hutan setiap tahunnya. Kebakaran hutan terjadi disebabkan oleh durasi musim panas yang terlalu lama dari waktu semestinya. Hutan merupakan tempat tinggal berbagai jenis satwa dan fauna yang memiliki banyak kekayaan hayati yang dapat membuat mereka bertahan hidup. Sering terjadinya kebakaran hutan menjadi isu lingkungan yang dianggap krusial dan mendapatkan perhatian baik dari tingkat lokal maupun internasional. Penelitian yang dilakukan ini menyajikan kajian klasifikasi wilayah titik api kebakaran hutan menggunakan salah satu algoritma Deep Learning (DL) yaitu metode Convolutional Neural Network (CNN), hal ini sangat dibutuhkan untuk pendahuluan mengenai peringatan dini kebakaran hutan yang ada di daerah tersebut. Wilayah titik api kebakaran hutan yang digunakan dalam penelitian ini dikumpulkan dari daerah Nusa Tenggara Timur (NTT), terutama pulau-pulau seperti Sumba dan Timor. Metode CNN melibatkan dua langkah utama. Langkah pertama adalah pengklasifikasian gambar melalui proses feedforward. Langkah kedua adalah fase pembelajaran menggunakan teknik backpropagation. Model CNN yang digunakan dalam proses pelatihan dataset menguji citra dengan beberapa pengoptimal dan diperoleh hasil akurasi yang tinggi. Kemiripan area yang terbakar dengan fitur terang lainnya mengurangi kepastian deteksi kebakaran hutan. Hasil penelitian menunjukkan bahwa Model CNN yang digunakan Untuk deteksi dan segmentasi area terbakar menggunakan algoritma terpilih, kinerja terbaik dengan pembelajaran mendalam yang dilaporkan dalam literatur adalah 89%.Teknik yang diusulkan dilatih menggunakan wilayah varian (kumpulan data) dan mengevaluasi presisi berdasarkan ambang recall, dengan akurasi keseluruhan 89%.
ANALISIS SENTIMEN TERHADAP DAMPAK PERANG ISRAEL - PALESTINA MELALUI DATA TWITTER MENGGUNAKAN NAIVE BAYES Halim, Alfian Noer; Dwiasnati, Saruni
FORMAT Vol 13, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i2.010

Abstract

The increasing development of information technology makes it easy for people to get various information only through social media such as Twitter. Twitter is a mainstay social networking application and source of information on world events. With Twitter, people can get a lot of the latest news. One piece of information that is widely discussed and is a trending topic on Twitter is the impact of the Israeli and Palestinian war. It is important to analyze the feelings of the impact of the ceasefire between Israel and Palestine from the amount of information in online media. The data used is Twitter, a social media platform. This research was conducted to analyze people's reactions to data in the form of tweets and group them according to the Naïve Bayes method into positive, neutral or negative opinions. In implementing the Naïve Bayes algorithm which uses 3 models of the Naïve Bayes algorithm, namely Gaussian, Multinomial, and Bernoulli, it shows different results, namely 50% for the Naïve Bayes Gaussian model, 57% for the Naïve Bayes Bernoulli model, and Naïve Bayes Multinomial model is 65 %. This shows that the Multinomial Naïve Bayes model is better than other models in classifying the data in this case.
PELATIHAN PEMANFAATAN LIMBAH KAIN PERCA Yuliarty, Popy; Dwiasnati, Saruni; Alfa, Bonitasari Nurul; Wijayanti, Atiek Ike
Jurnal Pengabdian Masyarakat Nasional Vol 3, No 2 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v3i2.21700

Abstract

Mata pelajaran Prakarya di Sekolah bertujuan untuk mengembangkan pengetahuan, keterampilan dan sikap percaya diri siswa melalui produk yang dihasilkan sendiri dengan memanfaatkan potensi sumber daya alam yang ada di lingkungan sekitar. Prakarya juga merupakan ilmu terapan yang mengaplikasikan pelbagai bidang ilmu pengetahuan untuk menyelesaikan masalah praktis yang secara langsung memengaruhi kehidupan kita sehari-hari. Luarannya diharapkan mampu mendidik siswa terampil dalam berbagai hal serta menumbuhkan jiwa wirausaha yang tentunya bermanfaat bagi mereka dalam hal  peningkatan ekonomi keluarga.Pihak mitra yang telah terikat kerjasama dalam bentuk agrrement, menyambut baik kegitan ini dengan harapan melaluai kegiatan ini dapat meningkatkan pengetahuan dan keterampilan siswa.Metode pelaksanaan dilakukan secara langsung berupa penyampaian materi dan praktek langsung tentang keterampilan seniu kreatif berupa tas dari anyaman pandan. Evaluasi kegiatan dilakukan dengan penyebaran kuisioner kepada para peserta untuk menilai kegiatan ini dengan hasil rata-rata adalah 4,9 dari skala 5 yang artinya sudah termasuk pada katagori sangat memuaskan. Kegiatan PPM ini dapat dilanjutkan dengan tema-tema atau topik-topik yang mendukung pelajar untuk dapat perduli kepada lingkungan dengan memanfaatkan ilmu pengetahuan yang di dapat di sekolah maupun dari sumber luar sekolah seperti Perguruan Tinggi.Luaran dari kegiatan ini adalah upload pada Youtube, publikasi pada media masa dan Jurnal Pengbdian Masyarakat.
PELATIHAN E-COMMERCE PADA MASYARAKAT DESA PASIR TANGERANG BANTEN DALAM UPAYA MENINGKATKAN DAYA SAING PENJUALAN PRODUK UMKM Riri Fajriah; Saruni Dwiasnati; Yuwan Jumaryadi
JURNAL SINERGI Vol. 6 No. 2 (2024): SINERGI
Publisher : FT-USNI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59134/sinergi.v6i2.670

Abstract

Pelatihan E-Commerce di Desa Pasir, Tangerang, Banten, dilaksanakan untuk meningkatkan daya saing produk UMKM lokal melalui pemanfaatan teknologi digital. Kegiatan ini bertujuan membekali pelaku UMKM dengan keterampilan pemasaran online, seperti penggunaan platform E-Commerce, teknik fotografi produk, strategi pemasaran digital, dan manajemen pesanan. Pelatihan diikuti oleh 50 peserta dengan metode ceramah, praktik langsung, dan pendampingan intensif. Hasil evaluasi menunjukkan adanya peningkatan signifikan dalam pemahaman peserta terhadap penggunaan teknologi digital untuk pemasaran. Dalam tiga bulan pasca pelatihan, terjadi peningkatan jumlah transaksi produk UMKM melalui platform online, yang menunjukkan keberhasilan kegiatan ini dalam mendorong transformasi digital UMKM. Program ini diharapkan dapat meningkatkan daya saing produk lokal Desa Pasir di pasar nasional secara berkelanjutan dan menjadi model pemberdayaan masyarakat berbasis teknologi untuk daerah lain.
Quantitative Analysis of Training Completion Using Multivariate Linear Regression Devianto, Yudo; Dwiasnati, Saruni; Gunawan, Wawan; Sumarto, Marco Alfan; Saputra, Dony Ramadhan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8851

Abstract

The urgency of this research stems from the strategic need to monitor and evaluate the achievements of digital training implemented by various academies under government coordination, including VSGA, FGA, DEA, TA, and GTA. In the context of the national digital transformation program, the availability of an analytical model that can predict the success of participants in completing training is critically crucial to support the achievement of the Ministry’s Key Performance Indicators (KPIs). The purpose of this study is to develop a predictive model based on multivariate linear regression that combines two main variables, the percentage of participants accepted and the percentage of participants who participate in onboarding, to project the level of training completion. This model is expected to provide a quantitative and objective assessment of the effectiveness of digital training implementation in each academy. The targeted outputs of this study include the development of a predictive model with performance validation through the calculation of R², which yielded a value of 0.9448, as well as the provision of technical reports and data-driven recommendations for enhancing digital training governance. The Technology Readiness Level (TKT) of this study is at TKT 3, and there is evidence of conceptual validation of the predictive model based on real data collected from the implementation of the training. This stage marks the readiness of the research to continue developing the system model and implementing it on the training evaluation platform in the next stage.
Detection of Rice Leaf Pests Based on Images with Convolution Neural Network in Yollo v8 Fauzi, Ahmad; Baihaqi, Kiki Ahmad; Pertiwi, Anggun; Devianto, Yudo; Dwiasnati, Saruni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2008

Abstract

Detection of rice leaf pests is important in agriculture because it can help farmers determine appropriate preventive measures. One method that can be used to detect rice leaf pests is digital image processing technology. In this research, proof of suitability for solving this case was carried out between the Convolutional Neural Network (CNN) algorithm which was run offline with R-CNN and YOLOv8 for detecting rice leaf pests. At the data preparation stage, images of rice leaves were taken from various sources with a total of 100 images taken from website data and 10 images taken from the research site. Next, preprocessing and data augmentation are carried out to improve image quality and increase data variation. At the model training stage, a training and evaluation process is carried out using two types of algorithms, namely R-CNN and YOLOv8. The accuracy of the testing results using the same data using Yolov8 obtained 87.0% accuracy and 79% precision, while using R-CNN the results obtained were 85% for accuracy and 75% for precision with data divided into 80 training data 20 validation data and 10 testing data. Labeling the dataset uses Makesensei which has been completely standardized, with the resulting parameters being the spots on rice leaves.
Penerapan Data Science untuk Mendukung Transformasi Digital UMKM di Kelurahan Kembangan Utara Dwiasnati, Saruni; Devianto, Yudo; Gunawan, Wawan; Yuliarty, Poppy
Kapas: Kumpulan Artikel Pengabdian Masyarakat Vol 4, No 2 (2025)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/ks.v4i2.4382

Abstract

The implementation of Data Science to support the digital transformation of SMEs (Small and Medium Enterprises) in Kelurahan Kembangan Utara aims to help local SMEs enhance their competitiveness through the utilization of digital technology. In the era of digitalization, SMEs need to adapt to changes in order to remain relevant and grow. Through the Data Science approach, this program focuses on utilizing data for market analysis, trend prediction, and business process optimization. Training provided to SME owners includes the application of data analysis algorithms, the creation of product recommendation systems, and the use of digital platforms that can improve operational efficiency and expand market reach. By integrating data-driven decision-making, SME owners can make more accurate decisions, increase sales, and open up new business opportunities. This program not only provides insights into the importance of digitalization but also equips participants with practical skills in using technologies relevant to the local market's needs. The expected outcome of this program is the improvement of the digital capacity of SMEs in Kelurahan Kembangan Utara, which in turn can contribute to the empowerment of the local economy.
Mapping Public Sentiment on Generative AI via Twitter NLP and Topic Modeling* Marcelino Caetano Noronha; Saruni Dwiasnati; Cherlina Helena P Panjaitan
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.183

Abstract

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *
Co-Authors Abdillah, Reza Wahyu Agung Susilo Yuda Irawan Ahmad Fauzi Ana Maria Ildiberta Da Costa Martins Anissa Lestari Antono, Fajar Ardy Triwibowo Arif , Sutan Mohammad ARIF, SUTAN MOHAMMAD Atiek Ike Wijayanti Aulia Naro Avrizal, Reza Aziz, Topaz Malik Bagus Budi Darmawan Baihaqi, Kiki Ahmad Bambang Jokonowo Bambang Sukowo Bonitasari Nurul Alfa Candra Setia Bakti Cherlina Helena P Panjaitan Devi Fitrianah Diah Utami, Diah Elga Ramdani Eliyani Eliyani Eliyani Fadli Nurrizky Fajar Antono Fajriah, Riri Fatimah, Nona Fatonah, Nenden Siti Fauzi Ahmad Muda Halim, Alfian Noer Hanny Hikmayanti Handayani Haris Yusuf Bakhtiar Hayu Kartika Herfandi Herfandi Herry Derajad Wijaya Herry Derajad Wijaya Hidayat, Rahmat Rian Ifan Prihandi Jumaryadi, Yuwan Kurnia Gusti Ayu Lokesworo M. Fajrun Nadhif Marcelino Caetano Noronha Medina Fibriyanti Akmal Muhaimin Hasanudin Muhammad Arya Hafsah Nurfiant Muhammad Rifqi muhammad rizky, muhammad Muhammad Taufiq Reza Nia Kusuma Wardhani Nicholas Reinaldo, Ryo Novera Elisa Triana Nur Ghazali Santoso Nur, Nur Umar Azaliy Awal Nurrizky, Fadli Pertiwi, Anggun Popy Yuliarty Popy Yuliarty, Popy Prihandani, Kamal Qitala, Shofia Reza Avrizal Rifqi Muhammad Rudiarto, Sabar Saputra, Dony Ramadhan Sarima Lumbansiantar Shofia Qitala Sonny Aditya Imawan Sukarna, Royan Habibie Sukma Wardhana Sumarto, Marco Alfan Surya Perdana Suzan Bernadetha Stephani Wardhana, Ariyani Wardhani, Elisa Wawan Gunawan Wawan Gunawan Wijayanti, Atiek Ike Yudo Devianto Yuliarty, Poppy