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All Journal Jurnal Simantec JURNAL REKAYASA TEKNOLOGI INFORMASI JURNAL EDUCATION AND DEVELOPMENT IJISTECH (International Journal Of Information System & Technology) JUTEKIN (Jurnal Manajemen Informatika) METIK JURNAL Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Jurnal Abdi Insani IJISTECH Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Teknosains Kodepena (Kodepena Journal of Technoscience) JAMINFOKOM Multica Science and Technology Jurnal Teknosains Kodepena (Kodepena Journal of Technoscience) Jurnal Manajemen Informatika & Teknologi JUPADAI : Jurnal Pengabdian Kepada Masyarakat LOFIAN: Jurnal Teknologi Informasi dan Komunikasi Asian Journal of Science, Technology, Engineering, and Art
Nariza Wanti Wulan Sari
Universitas Mulia
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

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Augmentasi Citra Pohon Kelapa Sawit untuk Deteksi Objek Berbasis Deep Learning Dedy Mirwansyah; Achmad Solichin; Fahrullah; Hardi, Richki; Wulan Sari, Nariza Wanti; Arista Riski, Nanda; Aldo, Dasril
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1001

Abstract

Penelitian ini menitikberatkan pada Augmentasi citra pohon kelapa sawit untuk deteksi objek menggunakan pendekatan Deep Learning. Pohon kelapa sawit memiliki peran penting dalam industri perkebunan dan pertanian, sehingga pengembangan metode deteksi pohon kelapa sawit yang efisien menjadi krusial dalam pemantauan perkebunan dan pengelolaan sumber daya alam. Metode penelitian melibatkan augmentasi citra, seperti flip, crop, hue, saturation, brightness, exposure dan pra-pemrosesan auto orient dan resize untuk meningkatkan kualitas data pelatihan. Model Deep Learning yang digunakan adalah Convolutional Neural Network (CNN) yang terintegrasi dengan teknik object detection, memungkinkan identifikasi pohon kelapa sawit dari latar belakang dengan akurasi tinggi. Penelitian ini menggunakan 101 citra kepala sawit dan setelah dilakukan augmentasi berjumlah 253 citra pohon kelapa sawit yang bervariasi dalam kondisi pencahayaan, sudut pandang, dan penutupan daun. Hasil eksperimen menunjukkan bahwa metode ini mampu mengidentifikasi pohon kelapa sawit dengan akurasi yang baik, bahkan dalam kondisi yang kompleks. Hasil penelitian ini memiliki potensi aplikasi dalam pemantauan perkebunan kelapa sawit, perencanaan lahan, dan pemantauan lingkungan. Dengan peningkatan akurasi deteksi dan ekstraksi, manajemen perkebunan dan pemantauan lingkungan dapat menjadi lebih efisien dan berkelanjutan.
PELATIHAN ANALISIS DATA DENGAN SOFTWARE R BAGI SISWA SMA NEGERI 8 SAMARINDA Sari, Nariza Wanti Wulan; Sifriyani, Sifriyani; Suyitno, Suyitno; Wahyuningsih, Sri; Yuniarti, Desi; Purnamasari, Ika; Mahmudah, Siti; Nurmayanti, Wiwit Pura; Widyaningrum, Erlyne Nadhilah; Nugraha, Pratama Yuly; Pangruruk, Thesya Atarezcha; Hidayanty, Nurul Ilma; Kosasih, Raditya Arya; Bahriah, Ayu
Jurnal Abdi Insani Vol 12 No 7 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i7.2136

Abstract

Students of SMA Negeri 8 Samarinda have received material on statistics since grade X. In the learning process, teachers use Microsoft Office Excel software which is closed source. So through this community service activity, a solution is provided by disseminating data analysis and alternative open source software 'R'. Community service activities are packaged in the form of training. Evaluation of activities in the form of pretest and posttest questionnaires and activity feedback surveys. This activity was carried out on September 11, 2024 in the Computer Laboratory Room of SMA Negeri 8 Samarinda. The number of students who participated in this activity consisted of 36 students. Based on the analysis of the pre-test and post-test data, it was concluded that there was an increase in student understanding after the training. The results of the feedback stated that the training material was easy, the explanations given were considered interesting, and the training activities were considered useful by the participants. Furthermore, participants hope that there will be follow-up activities to hold similar activities again.
IMPLEMENTATION OF NEURAL NETWORK IN PREDICTING STOCK PRICE OF PT BANK RAKYAT INDONESIA (PERSERO) TBK Nurmayanti, Wiwit Pura; Ni Luh Desvita Pratiwi; Nariza Wanti Wulan Sari; Desi Yuniarti; Erlyne Nadhilah Widyaningrum; Thesya Atarezcha Pangruruk
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/dwkza342

Abstract

Forecasting involves estimating future outcomes by examining patterns in both historical and present data. A commonly used data type in forecasting is time series data, characterized by observations collected at consistent time intervals. One forecasting technique that has gained significant attention is the Neural Network, particularly through the Backpropagation method utilized in this study. In the context of the stock market, price fluctuations are influenced by a variety of factors, including shareholder rights, company performance, and the balance between supply and demand. Typically, a rise in stock prices leads to decreased demand, while a decline tends to stimulate it. Predicting stock prices, such as those of Bank Rakyat Indonesia (BRI), can support investors in making well-informed decisions. This research seeks to identify the optimal number of neurons in the hidden layer for forecasting BRI stock prices by minimizing error metrics such as MAPE, MSE, and MAE. The analysis revealed that forecasting the stock price of PT Bank Rakyat Indonesia (Persero) Tbk. using a neural network with one hidden neuron resulted in a MAPE of 1.22248 and an MAE of 61.30548.
TRAFFIC ACCIDENT VICTIM CLASSIFICATION IN BONTANG USING NW-KNN AND BACKWARD ELIMINATION Mangalik, Gerald; Nariza Wanti Wulan Sari; Surya Prangga; Wiwit Pura Nurmayanti; Ika Purnamasari
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/yfbspb33

Abstract

Traffic accidents have been a serious problem caused by various factors such as road conditions, driver behavior, and weather. To understand the pattern of victim severity, a classification approach capable of handling imbalanced data and irrelevant features was needed. This study aimed to classify the status of accident victims using the Neighbor Weighted K-Nearest Neighbor (NW-KNN) method, equipped with backward elimination for feature selection. Backward elimination was employed to reduce insignificant features and improve accuracy.The case study for this research involved the status of accident victims in Bontang City, with a sample size of 93 cases. There were nine features in this study: accident victim status, accident time, road density, road function, road surface condition, speed limit at the location, road slope, and road status.The research results showed that the best parameter combination for classification using the NW-KNN method with backward elimination was K = 7 and E = 3. The "type of accident" feature was eliminated, leaving 8 features. Classification results using the NW-KNN method with backward elimination yielded an accuracy of 88.89%, demonstrating an improvement in classification performance for identifying the status of traffic accident victims. Thus, this method proved to be an effective approach for traffic accident analysis in Bontang City.
Co-Authors Abdul Rahim Abdurahman Achmad Asdori Achmad Solichin Agung Ranggono Aisyiyah Maulisa Nuur Anam, M Khairul Arief Hidayat Arista Riski, Nanda Bahriah, Ayu Budi, Ennesya Estya Dasril Aldo Dedy Mirwansyah Dedy Mirwansyah Desi Yuniarti Dody Novandi Dody Prasetia Purnama Erlyne Nadhilah Widyaningrum Fahrullah Fahrullah Fahrullah, Fahrullah Faldi Fanny Pratiwi Fardiansyah Ibrahim Faza Alameka Gubtha Mahendra Putra Hairah, Ummul Hambali, Nidaa Rifdah Rahima Heri Yansah Hidayanty, Nurul Ilma Ika Purnamasari Ika Purnamasari Indah Ayu Maharani Indra Dwi Pangestu Ivan Leonard Tandi Kosasih, Raditya Arya Mangalik, Gerald Marcelino Irawan, Kenny Maulidin Akbari Muhammad Muhammad Adryan Pratama Muhammad Bambang Firdaus Muhammad Ferdiansyah Muhammad Rayda Bazhrullah Muhammad Wisdan Pratama Putra Muhammad Yani Muhyiddin Muhyiddin Mutmainnah, Dalfa Diandra Natanael Dewanto Dewanto Ni Luh Desvita Pratiwi Nugraha, Pratama Yuly Nurmayanti, Wiwit Pura Palilu, Dryan Putra Pangruruk , Thesya Atarezcha Pangruruk Putriani Putriani Riawan, Tommy Andi Richki Hardi Rika Ismayanti Riyayatsyah Riyayatsyah Riyayatsyah Rizal Putra Aditya Sifriyani, Sifriyani Sigit Pangestu SITI MAHMUDAH Sri Wahyuningsih Sry Tualeka Sulfikar Rakita Dewa Surya Prangga Suyitno Suyitno Tejawati, Andi Thami Rusdi Agus Thesya Atarezcha Pangruruk Tina Tri Wulansari Tri Wulansari, Tina Wahyu Yanuartha Wiji Prima Fitriyanto Wiwit Pura Nurmayanti Wulansari, Tina Tri Yumami, Eva Yuniarti, Desi ZIDAN, Muhamad Nur Zidan