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All Journal Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Jurnal sistem informasi, Teknologi informasi dan komputer E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Knowledge Engineering and Data Science Wikrama Parahita : Jurnal Pengabdian Masyarakat Empowerment : Jurnal Pengabdian Masyarakat JUTIK : Jurnal Teknologi Informasi dan Komputer MATAPPA: Jurnal Pengabdian Kepada Masyarakat JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) GANEC SWARA EDUKATIF : JURNAL ILMU PENDIDIKAN Jurnal Teknologi Terpadu Jurnal Pengabdian Dharma Laksana Bantenese Jurnal Pengabdian Masyarakat Jurnal Kridatama Sains dan Teknologi To Maega: Jurnal Pengabdian Masyarakat Jurnal Mantik Abdi Dosen : Jurnal Pengabdian Pada Masyarakat Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika KAIBON ABHINAYA : JURNAL PENGABDIAN MASYARAKAT Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Journal of Intelligent Decision Support System (IDSS) Jurnal Peduli Masyarakat Reswara: Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Global Health research BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Journal of Applied Data Sciences J-Eltrik Jurnal Info Sains : Informatika dan Sains TIERS Information Technology Journal Jurnal Ilmiah Telsinas Elektro, Sipil dan Teknik Informasi Journal of Language and Health J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Jurnal Ilmiah Teknik Informatika dan Komunikasi Archipelago: Jurnal Pengabdian Kepada Masyarakat Iain Ternate
Adie Wahyudi Oktavia Gama
Universitas Pendidikan Nasional
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Identifying Key Factors Causing Flooding Using Machine Learning Gama, Adie Wahyudi Oktavia; Dennatan, Monalisa; Dharmayasa, I Gusti Ngurah Putu; Maw, Me Me; Sugiana, I Putu; Suryanti, Irma
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.463

Abstract

The impact of flooding extends beyond physical and infrastructural damage, affecting social, economic, and environmental dimensions. This study aims to identify the key factors influencing flooding by developing a decision tree model. The research method applies the C4.5 algorithm to build a decision tree model using flood factors such as rainfall, soil type, elevation, land use, and distance from rivers. The model is then applied to 57 past flood data events to determine key contributors to flooding in Denpasar City, Bali, Indonesia. The analysis showed that land elevation is the most influential factor, with areas below 28 meters above sea level having a 71% likelihood of being flood vulnerability. Additionally, the model reveals unknown patterns contributing to flood vulnerability among the factors considered. These insights give a deeper understanding of how these factors combine to affect flood vulnerability. The model's effectiveness was evaluated using a confusion matrix, resulting in an accuracy rate of 90%, a precision rate of 100%, a sensitivity rate of 90%, a specificity rate of 100%, and a F1 Score rate of 94%, demonstrating its strong predictive power in identifying areas at risk of flood vulnerability. Although this study is limited by the availability of data, the focus on Denpasar City, and the potential omission of other relevant attributes, it advances flood risk assessment by applying machine learning to provide practical insights that could enhance flood management strategies, with potential applications to other urban areas facing similar risks.
Expert System For Early Diagnosis Of Dog Skin Diseases Using The Dempster-Shafer Method Adie Wahyudi Oktavia Gama; Putri Prema Paramitha; Ni Made Widnyani
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 04 (2024): Informatika dan Sains , 2024
Publisher : SEAN Institute

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

Abstract

This study developed a web-based expert system for the early diagnosis of canine skin diseases, utilizing the Dempster-Shafer method. The close bond between humans and dogs, the high rate of dog ownership, and the prevalence of skin diseases among dogs in Bali Province highlight the need for an effective solution, particularly given dog owners' limited knowledge of diagnosis and treatment options. The research involved interviews with veterinary experts and an extensive literature review, resulting in the creation of a knowledge base comprising 12 common canine skin diseases and 23 associated symptoms. The Dempster-Shafer method was employed to address uncertainty in decision-making by calculating belief and plausibility values, ensuring accurate diagnostic results. The system's database design enables efficient management of disease, symptom, patient, and diagnosis information. Testing demonstrated an accuracy rate of 86.36% and a user satisfaction score of 89.09%, indicating that the system is both reliable and user-friendly. This expert system provides a practical tool for dog owners in Bali, supporting early diagnosis and appropriate management of canine skin diseases.
Upaya Meningkatkan Partisipasi Pemilih Pemula Pemilu 2024 Melalui Sosialisasi Surat Suara di SMA PGRI 2 Denpasar Davi, Nadine Kalina; Wibawa Putra, Komang Satria; Ariwangsa, I Gusti Ngurah Oka; Gama, Adie Wahyudi Oktavia; Permana, Gusi Putu Lestara
KAIBON ABHINAYA : JURNAL PENGABDIAN MASYARAKAT Vol. 7 No. 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/7e275t50

Abstract

The 2024 elections to elect the President and Vice President, Members of the DPR RI, Members of the DPD RI, Members of the Provincial DPRD, and Members of the Regency/City DPRD will be held simultaneously throughout Indonesia on February 14th, 2024. According to data from the General Election Commission (KPU), the Millennial and Z generations dominate the vote in this election with 114 million voters or approximately with a total of 56% of all voters, so it can be ensured that for the success of the election, the participation of these two generations is very important. The service method was carried out by providing election outreach to 100 students at SMA PGRI 2 Denpasar on Tuesday, January 30 2024 at 10.30 - 11.00 WITA with the stages of providing material and a question and answer session. The results obtained were that many students already knew that the 2024 elections would take place and the location of each voting place. However, most students do not know details such as the types of ballot papers and the administrative procedures for transferring votes. After the socialization activities were completed, the students understood the message that their voting rights were important and that they should not abstain.
Penyuluhan Money Politic Bagi Masyarakat Desa Dangin Puri Kaja Dalam Menghadapi PEMILU 2024 Maharani, Faradita Putri Aura; Gama, Adie Wahyudi Oktavia
Reswara: Jurnal Pengabdian Kepada Masyarakat Vol 5, No 2 (2024)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v5i2.4335

Abstract

Praktik politik uang masih menjadi permasalahan besar dalam menghadapi Pemilu 2024. Merebaknya kasus politik uang dikhawatirkan akan menciptakan banyak pejabat publik yang korupsi dan merusak kualitas demokrasi. Pada kenyataanya, masih banyak masyarakat yang enggan menolak politik uang secara tegas. Hal tersebut dikarenakan minimnya edukasi dan kesadaran masyarakat mengenai politik uang. Berdasarkan temuan tersebut, peningkatan edukasi serta kesadaran masyarakat terkait politik uang perlu dilakukan. Tujuan kegiatan pengabdian kepada masyarakat ini, diharapkan dapat meningkatkan kesadaran masyarakat untuk menolak politik uang dalam menghadapi Pemilu 2024. Metode kegiatan pengabdian masyarakat dilakukan dengan cara penyuluhan mulai dari observasi, persiapan, menyebarkan brosur dan kuisioner ke banjar-banjar, serta evaluasi. Pengabdian kepada masyarakat ini dilaksanakan mulai tanggal 15 Januari – 1 Maret 2024 di Desa Dangin Puri Kaja, Kecamatan Denpasar Utara, Kota Denpasar, Provinsi Bali. Hasil dan analisis menunjukkan, kegiatan program kerja ini berhasil mengedukasi partisipan sejumlah 15 orang mengenai pengertian, bentuk politik uang, dampak, hukumnya, dan meningkatkan kesadaran untuk menolak politik uang secara tegas.
HOG feature extraction in optimizing FK-NN and CNN for image identification of rice plant diseases Gama, Adie Wahyudi Oktavia; Gunawan, Putu Vina Junia Antarista; Darmaastawan, Kadek
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.722

Abstract

This study compares the performance of FK-NN and CNN models in identifying rice diseases from digital images, focusing on both effectiveness and efficiency. Additionally, this research utilizes HOG for feature extraction from the digital images. The stages include data collection, preprocessing, transformation, classification, and model evaluation. The results show that the FK-NN model achieves a higher accuracy of 86.26%, compared to the CNN model's accuracy of 71.37%. Furthermore, the precision value of the FK-NN model is also higher at 86.88%, compared to the CNN model’s precision of 72.74%. Similarly, the recall value for the FK-NN model is higher at 86.88%, compared to the CNN model’s 71.37%. The F1-score of the FK-NN model is likewise superior, with a value of 86.88%, compared to the CNN model’s F1-score of 71.37%. These findings suggest that the FK-NN model with HOG feature extraction is more effective. However, in terms of inference time, the CNN model is faster, taking 0.000282 seconds compared to FK-NN’s 0.002331 seconds. In conclusion, the FK-NN model with HOG feature extraction excels in identifying rice diseases, while the CNN model offers faster inference time in this study.
Optimizing the Productivity of the Tandusan Oil Home Industry in Jembrana Through the Implementation of a Digital Platform Widayanti, Ni Putu Widayanti; Widnyani, Ni Made; Darma, I Gede Wahyu Surya; Gama, Adie Wahyudi Oktavia
Wikrama Parahita : Jurnal Pengabdian Masyarakat Vol. 9 No. 1 (2025): May 2025
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jpmwp.v9i1.9448

Abstract

The Tandusan oil household industry in Baluk-Jembrana Village is a form of local business that produces organic coconut oil. The problem faced by this industrial group is a lack of understanding of product quality and a lack of knowledge in marketing digital-based products. The proposed solution includes education on improving product quality based on predetermined chemical parameters and intensive training in digital platform-based product marketing. The methods used in this activity are Participation Action Research (PAR). This activity's success level is measured through the pretest and posttest. This activity was carried out on September 2, 2023. The number of partner workers who participated in this training activity was 10. With this training, partners' knowledge of product quality increased by eighty-two percent, while partners' knowledge increased by eighty-three percent regarding digital platform-based product marketing.
Penerapan Algoritma Naïve Bayes untuk Prediksi Penyakit Depresi pada Mahasiswa gama, Adie Wahyudi Oktavia; Grren, Agustini Degni Melsy; Paramartha, I Gusti Ngurah Darma; Prathama, Gede Humaswara; Widnyani, Ni Made; Dananjaya, Md. Wira Putra
Journal of Language and Health Vol 6 No 2 (2025): Journal of Language and Health
Publisher : CV. Global Health Science Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37287/jlh.v6i2.6926

Abstract

Depression is one of the most serious mental health problems among college students, but it often goes unnoticed due to social stigma and limited access to psychological services. This study aims to apply the Naïve Bayes algorithm to predict depression in college students based on various factors, such as academic pressure, sleep duration, eating habits, financial stress, and family history of mental disorders. The model was built using 502 data obtained from the Kaggle platform, through the stages of data preprocessing, transformation, classification using Gaussian Naïve Bayes, and evaluation using a confusion matrix. The implementation process was carried out in Google Colab using the scikit-learn library. The evaluation results showed very good model performance with an accuracy of 97%, precision of 96%, recall of 98%, and F1-score of 97%. These findings indicate that the Naïve Bayes algorithm can be used effectively as an anonymous and efficient early screening tool for depression and has the potential to support increased awareness and mental health interventions in the college student environment.
SPAM EMAIL CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) AND TF-IDF: A CASE STUDY WITH THE TREC 2007 AND ENRON-SPAM DATASETS Paramartha, I Gusti Ngurah Darma; Sudestra, I Made Ardi; Gama, Adie Wahyudi Oktavia; Prathama, Gede Humaswara
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.22770

Abstract

Spam emails represent a substantial concern within the digital landscape, impeding users with unsolicited communications. This study elucidates the utilization of a Support Vector Machine (SVM) coupled with a TF-IDF Vectorizer for categorizing emails into spam and non-spam classifications. The model was developed utilizing two publicly accessible pre-processed datasets: the TREC 2007 Public Spam Corpus and the Enron-Spam Dataset. By employing the TF-IDF algorithm, which allocates heightened importance to infrequent yet pertinent terms, alongside SVM, renowned for its efficacy in textual classification, the model exhibits remarkable efficacy, achieving an accuracy of 99.04%, a precision of 98.57% and a recall of 99.62%. These findings underscore the model's formidable capacity to discern spam emails while concurrently minimizing false positives accurately. This is critical for real-world applications where authentic emails must not be erroneously categorized as spam. Furthermore, this study elaborates on the justification for the selection of TF-IDF and SVM in the context of spam email classification, in addition to the evaluation outcomes of the model, which align with existing literature, wherein the integration of SVM with TF-IDF has demonstrated substantial performance in spam detection endeavours.
IMPLEMENTASI DATA MINING DALAM MENENTUKAN TATA LETAK PRODUK MENGGUNAKAN ALGORITMA FP-GROWTH Prathama, Gede Humaswara; Devi Anggreni, Ni Komang Ayu; Oktavia Gama, Adie Wahyudi; Darma Paramartha, I Gusti Ngurah
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.22855

Abstract

This study analyzes purchasing patterns in minimarkets using the FP-Growth algorithm to optimize product layouts. One year of sales transaction data (103,181 transactions) from UNDIKNAS Mart were analyzed through data cleaning, transformation, and aggregation. The FP-Growth algorithm was applied with minimum support 5%, confidence 80%, and lift >1 thresholds. Results identified strong product associations, particularly between "Aqua 600 ml (Tanggung)" and various snacks, with confidence values of 81-93% and lift >5. Implementing these findings in product arrangement increased sales by 15-20% despite store accessibility limitations. Cross-validation using a decision tree model showed 81.67% accuracy. The findings demonstrate FP-Growth's effectiveness in small-scale transaction data analysis. The research provides practical contributions for retailers to boost sales through data-driven product layout optimization. A limitation is the single-location data scope, suggesting the need for broader subsequent studies. This study offers a data-based approach adoptable by small and medium retail businesses to enhance operational efficiency and profits. The research confirms that data mining techniques can significantly impact retail performance even in constrained environments, providing empirical evidence of FP-Growth's practical utility in real-world minimarket settings. The methodology and findings contribute to the growing literature on data mining applications in small-scale retail operations, offering replicable frameworks for similar business contexts.
Sistem Monitoring Penyiraman Otomatis Tanaman Bunga Gemitir Menggunakan Aplikasi Mobile dan Web Thingspeak Sutama, I Wayan; Gama, Adie Wahyudi Oktavia; Negara, I Gede Artha; Wisesa, I Dewa Putu Arjun Suhartana
Jurnal Ilmiah Telsinas Vol 4 No 2 (2021)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1202.626 KB) | DOI: 10.38043/telsinas.v4i2.3197

Abstract

The role of technology is very important in an automatic watering monitoring system that works based on the YL69 sensor and DHT11 sensor. This study aims to determine the working principle of sensors in a plant watering monitoring system with mobile applications (Blynk) and thingspeak web. Thingspeak web design aims to process data recording. To process and dispatch YL69 sensor data and DHT11 sensor, the Arduino UNO microcontroller is used as well as a relay as a switch to drain and disconnect the electric current to the water pump. The design results show that this automatic plant watering monitoring system can work well, where the soil moisture is below <50%, the water pump will live "ON" and if the soil moisture is above> 60% the water pump will die "OFF". From the results of the design and testing of the monitoring system carried out it is able to monitor data from the YL69 sensor, DHT11 sensor and pump conditions in real-time with the mobile application (Blynk). While the thingspeak web can monitor and record YL69 sensor data, DHT11 sensors and pump conditions every 20 seconds..
Co-Authors Adhiya Garini Putri, Dewa Ayu Agus Ariana, I Komang Ajeng Ayu Fitri Ariatmaja Ariesta, Ni Luh Wina Sinta Ariwangsa, I Gusti Ngurah Oka Arta, Kadek Ananda Dwi Pebri Arya Putra Sanjaya, I Ketut Gede Bunga, Melania Pritama Danang Utomo Dananjaya, Md. Wira Putra Darma, I Gede Wahyu Surya Darmaastawan, Kadek Davi, Nadine Kalina Dennatan, Monalisa Devi Anggreni, Ni Komang Ayu Devi, Ni Kadek Sintya Dewa Ayu Putu Adhiya Garini Putri Dewa Gede Hendra Divayana, Dewa Gede Hendra Dewi Puspita Ningrat, Qorry Dharma, I Kadek Dwi Yudiarsana Diantari, Putu Yuliska Dwi Sanjani Mertaningsih, Ni Kadek Gede Hendra Divayana, Dewa Gede Humaswara Prathama Ginanita Utami, Cokorda Istri Ustana Grren, Agustini Degni Melsy Gunanti, A A Istri Indah Paristya Gunawan, Putu Vina Junia Antarista Gusi Putu Lestara Permana Gusti Ngurah Darma Paramartha, I Hari Putri, Tasya Prajna Pratisthita Hayu Mas Wrespatiningsih I Dewa Putu Arjun Suhartana Wisesa I Gede Artha Negara I Gusti Ayu Cintya Wardani I Gusti Ayu Intan Candra Dewi I Gusti Ngurah Darma Paramartha I Gusti Ngurah Putu Dharmayasa I Gusti Putu Riyan Nugraha I ketut Gede Darma Putra I Made Ardana I Made Riski Aditya Darma I Made Sudiksa I Made Wirya Darma I Nyoman Gde Artadana Mahaputra Wardhiana I Nyoman Hary Kurniawan I Putu Agung Bayupati I Putu Wisna Ariawan I Wayan Abimayu Angga Nugraha I Wayan Aditya Suranata I Wayan Dikse Pancane I Wayan Sukadana I Wayan Sukadana I Wayan Sutama I Wayan Sutama Irma Suryanti Ivan Surya Pramana Putra, Kadek Bagus John Junieargo Timotius John Timotius Junieargo Kadek Devi Kalfika Anggaria Wardani Kadek Devi Kalfika Anggria Wardani Kadek Prasilia Candra Dewi Komang Bagus Novan Bayu Pramana Putra Kurniawan, I Nyoman Hary Lin, Fanny Made Jana Narendra Made Widnyani, Ni Maharani, Faradita Putri Aura Maulidan, Bagus Maw, Me Me Negara, I Gede Artha Negara, Komang Ayu Aprillia Puspa Ngakan Nyoman Kutha Krisnawijaya Ngurah Komang Wiradnyana Ni Kadek Nadya Kartika Paramita Ni Made Ochiana Septhi Pratiwi Ni Nyoman Triana Margareta Ni Putu Jenifer Febriari Ni Putu Widayanti Ni Wayan Rena Mariani Nilton Da Conceicao Marques Nimadeni Yuniartika Nur Aprilya, Fira Nurullita Wardani, Venti Oktama Setyawan, I Kadek P. WAYAN ARTA SUYASA Permana, Putu Indra Pertama, Gusti Putu Lestara Praditya Maha Wiguna, I Made Putra, Komang Satria Wibawa Putri Prema Paramitha Putu Emy Samiadnyani Putu Purnama Dewi Putu Riska Indah Mentari putu suparna, putu Sastra Dewanti, Wayan Ari Sudestra, I Made Ardi Sugiana, I Putu Sugiharni, Gusti Ayu Dessy Suputra, Komang Yudi Swari, Luh Gede Widi T Krisna Narayana, Made Gede Bagus Wardhiana, I Nyoman Gde Artadana Mahaputra Wardhiana, Nyoman Dana Wayan Sugandini Widnyani, Ni Made Wisesa, I Dewa Putu Arjun Suhartana