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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Teknologi dan Manajemen Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding SNATIF Sistem : Jurnal Ilmu-Ilmu Teknik JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) Conference SENATIK STT Adisutjipto Yogyakarta Management and Economics Journal (MEC-J) JTAM (Jurnal Teori dan Aplikasi Matematika) CYCLOTRON Jurnal Abadimas Adi Buana Jiko (Jurnal Informatika dan komputer) Jurnal Teknik Elektro dan Komputer TRIAC Jurnal Riset Informatika JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech Jurnal Sistem informasi dan informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) CIVITAS (JURNAL PEMBELAJARAN DAN ILMU CIVIC) International Journal of Advances in Data and Information Systems Journal of Computer Networks, Architecture and High Performance Computing Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Teknik Informatika (JUTIF) International Journal of Robotics and Control Systems Jurnal Teknologi dan Manajemen SinarFe7 Jurnal Penelitian Journal of Information Systems and Technology Research JAPI: Jurnal Akses Pengabdian Indonesia Internet of Things and Artificial Intelligence Journal JEECS (Journal of Electrical Engineering and Computer Sciences) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi ITIJ Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal ilmiah teknologi informasi Asia
Dwi Arman Prasetya
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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PEMANFAATAN ENERGI HIBRIDA ANGIN DAN TENAGA SURYA DI DESA BATANG-BATANG DAYA SUMENEP Nurcahyanie, Yunia Dwie; Rochman, Sagita; Rukmana, Siti Nuurlaily; Prasetya, Dwi Arman; Rosariawari, Firra; Jariyah
Jurnal Abadimas Adi Buana Vol 7 No 02 (2024): Jurnal Abadimas Adi Buana
Publisher : LPPM Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/abadimas.v7.i02.a8239

Abstract

Pendistribusian air ke masyarakat warga Desa Batang-Batang Daya dengan memanfaatkan teknologi pompa hibrida, yaitu dengan menggunakan energi tenaga angin dan tenaga matahari. Pompa ini cocok untuk dimanfaatkan di area permukiman dengan potensi alam angin dan matahari. Pompa hibrida di desain untuk menutupi kekurangan energi dari masing-masing sumber, dimana jika hembusan angin tidak stabil dapat dipenuhi dengan energi matahari, ataupun sebaliknya. Sistem hibrid turbin angin dan solar sel mampu menghidupkan pompa air, sehingga pompa bisa mengalirkan air dari sumur ke tandon air. Pengolahan listrik tenaga surya memerlukan sistem otomatis dengan memutus sirkulasi sistem pengisian baterai jika sudah penuh, hal ini bertujuan untuk menghindari kerusakan pada baterai. Selanjutnya disambungkan dengan pompa, sensor dan modem internet agar dapat dimanfaatkan dengan koneksi IoT. Dengan koneksi IoT ini dapat dilakukan monitor dengan penggunaan smartphone untuk data indikator sensor debit, kelembaman tanah dan kecepatan angin. Berdasarkan hasil di lapangan bahwa terjadi peningkatan jumlah kebutuhan air bersih sebesar 50% dengan penggunaan sumur bor dan pompa hibrida. Hal ini tentu produktivitas kinerja petani dan peternak sapi di Desa Batang Batang Daya meningkat sebesar 70%
FORECASTING THE OCCUPANCY RATE OF STAR HOTELS IN BALI USING THE XGBOOST AND SVR METHODS Damaliana, Aviolla Terza; Muhaimin, Amri; Prasetya, Dwi Arman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 1 (2024): Jurnal Statistika Universitass Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.12.1.2024.24-33

Abstract

The hotel occupancy rate indicator has become a concern in recent years as it goes hand in hand with the rapid growth of the global tourism industry. A way to maintain or even improve this indicator is to carry out managerial planning using forecasting methods. The forecasting methods used in this research are XGBoost and SVR. The advantage of this modelling is that it achieves high accuracy and processing speed. Meanwhile, the benefit of SVR is that it will produce good prediction because can overcome overfitting. The steps in this research are exploring data, separating training data and testing data, transforming data, modelling data, forecasting data, and evaluating forecasting results using RMSE, MAE, and MAPE. The results show that MAPE value from both methods is smaller than 10%, which means that both methods can predict the occupancy rate of star hotels in Bali very accurately. Apart from that, the SVR method has smaller values for all model evaluation criteria than the XGBoost method, which means that the SVR method is better than XGBoost for predicting the occupancy rate of star hotels in Bali.
Impact of Smart Greenhouse Using IoT for Enhanced Quality of Plant Growth Ali, Munawar; Gunawan, Anak Agung Ngurah; Prasetya, Dwi Arman; Ibrahim, Mohd Zamri Bin; Diyasa, I Gede Susrama Mas
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1277

Abstract

Greenhouses play a crucial role in manipulating environmental conditions for optimal plant growth. While existing greenhouses enhance control over environmental factors, manual controls such as watering and humidity regulation often lead to suboptimal production and increased costs. This study proposes the development of a smart greenhouse with an automatic control system using fuzzy logic, specifically fuzzy Sugeno, to regulate watering and lighting based on soil moisture, temperature, and light intensity. The system's architecture involves sensor inputs, microcontroller processing, and the activation of actuators, such as UV lights and water pumps. Fuzzy logic is applied to interpret soil moisture and temperature inputs and determine optimal irrigation durations. The system's functionality is tested and validated through functional testing, Blynk application testing, and fuzzy Sugeno testing. Results indicate the successful implementation of the proposed smart greenhouse system. Functional testing demonstrates accurate sensor readings, including temperature and soil moisture. The Blynk application enables real-time monitoring and control of environmental conditions. Fuzzy Sugeno testing validates the irrigation control system, with an average error rate of 1.3%, affirming the system's alignment with desired specifications. Plant testing in different conditions showcases the effectiveness of the smart greenhouse in supporting plant growth and development.
Skrining Pencegahan Penularan TBC (SiGaP-TBC) di Pondok Pesantren Hidayatulloh Al-Muhajirin, Bangkalan Salim, Hotimah Masdan; Mulyadi, Mulyadi; Prasetya, Dwi Arman
Darmabakti : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 6 No 01 (2025): Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat
Publisher : Lembaga Peneliian dan Pengabdian Masyarakat (LPPM) Universitas Islam Madura (UIM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/darmabakti.2025.6.01.44-51

Abstract

Pondok Pesantren Hidayatullah Al-Muhajjirin accommodates approximately 500 male and female students aged 14 to 18, offering education at the Madrasah Tsanawiyah and vocational high school (SMK) levels. Given the diverse backgrounds of these students, their awareness of Clean and Healthy Living Behavior (PHBS) varies, impacting efforts to prevent airborne diseases like tuberculosis (TB). To address this, a community service initiative introduced the SiGaP-TBC program, aiming to enhance knowledge about TB transmission and promote self-screening. Methods included educational sessions, health screenings, utilization of the SiGaP-TBC application for self-assessment, and training in food self-sufficiency through healthy gardening. The program led to increased student awareness of PHBS in TB prevention, the establishment of student health cadres within the pesantren's health management system, and improved self-screening capabilities using the SiGaP-TBC app. In conclusion, the SiGaP-TBC program effectively fosters self-reliance in TB infection prevention and health empowerment within the pesantren community.
IMAGE CLASSIFICATION OF VINE LEAF DISEASES USING COMPLEX-VALUED NEURAL NETWORK Putri, Irma Amanda; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7809

Abstract

Leaf diseases are a serious challenge in the agricultural industry affecting crop quality and yield especially in grapevines. Early recognition and classification of grape leaf diseases is crucial to enable farmers to take appropriate preventive measures in maintaining the health of their crops. The research utilized an innovative approach based on Complex-Valued Neural Network (CVNN) to address the problem. Using Complex-Valued Neural Network (CVNN) this research seeks to identify and classify grape leaf diseases through a series of experiments. A total of 100 images divided into 4 classes namely Black Rot, ESCA, Leaf Blight, and Healthy were collected to train the model. The results show that the trained CVNN model successfully achieved a training accuracy of 100% and a testing accuracy of 97%, demonstrating excellent performance in classifying grape leaf diseases. This states that the proposed approach has great potential to be an effective tool in helping growers manage their vineyards more efficiently and effectively. The developed image processing method is expected to be applied in designing a system to perform image classification of diseases on grape leaves.
COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Maulidiyyah, Nova Auliyatul; Trimono, Trimono; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.
CLASSIFICATION OF JAVANESE NGLEGENA SCRIPT USING COMPLEXVALUED NEURAL NETWORK Rahmawati, Adinda Aulia; Muhaimin, Amri; Prasetya, Dwi Arman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7808

Abstract

Javanese script is one of the traditional scripts in Indonesia used by the Javanese people. The Javanese script used in Javanese spelling basically consists of 20 main characters (nglegena), namely from the Ha to Nga script. Javanese script has very high value, the uniqueness of the script is one thing that must be preserved. However, widespread use of Javanese script has declined as technology has developed. In this context, one of the problems that arises is the difficulty in automatically recognizing and classifying the Javanese Nglegena script. Therefore, the use of computational methods to automatically classify the Nglegena Javanese script is very important. This research compares 2 methods for classifying Javanese Nglegena script, namely Complex-Valued Neural Network (CVNN) and Convolutional Neural Network (CNN). This research aims to compare the best accuracy between CVNN and CNN. In this study, the Complex-Valued Neural Network method had a higher average accuracy, namely 96.332% and a loss of 0.1834. Meanwhile, the CNN method has an average accuracy of 93.72% and a loss of 0.4254. Artificial intelligence-based Javanese Nglegena script classification technology can help people to recognize the Javanese Nglegena script, especially in the fields of education and culture.
Prediksi Gangguan Kesehatan Mental pada Kalangan Mahasiswa Menggunakan Metode Pseudo-Labeling dan Algoritma Regresi Logistik Sari, Anggraini Puspita; Prasetya, Dwi Arman; Aditiawan, Firza Prima; Al Haromainy, Muhammad Muharrom
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp40-48

Abstract

Mental illness is a health condition that alters a person's thoughts, feelings, or behaviors, leading to distress and difficulty in maintaining a normal life. Mental health issues should not be taken lightly due to the challenges associated with diagnosis. Many students tend to experience mental health problems at various stages of their education, from diploma programs to doctoral studies. This situation becomes more critical as students approach the end of their studies and anticipate future prospects. This article explores the mental health status of students through symptoms, using logistic regression methods for prediction based on the dataset used. In this study, two types of data are employed: labeled dataset and unlabeled dataset, which are combined to create a semi-supervised learning approach. Labeled dataset is classified using a logistic regression algorithm, while unlabeled dataset employs the pseudo-labeling method. The analysis and modeling of the dataset indicate that the comparison between labeled and unlabeled dataset can significantly affect accuracy and processing time. Furthermore, the use of the pseudo-labeling method with the logistic regression algorithm is well-suited for the mental health case study, achieving an accuracy of 98% with a labeled to unlabeled dataset ratio of 1:2.
Detection of Abnormal Human Sperm Morphology Using Support Vector Machine (SVM) Classification Mas Diyasa, I Gede Susrama; Prasetya, Dwi Arman; Cahyani Kuswardhani, Hajjar Ayu; Halim, Christina
Information Technology International Journal Vol. 2 No. 2 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i2.36

Abstract

Abnormal sperm morphology is a key indicator of male infertility, making its accurate detection crucial for reproductive health assessments. This study explores the application of Support Vector Machine (SVM) classification to automatically detect abnormalities in human sperm morphology. A dataset of microscopic sperm images was collected and labelled based on normal and abnormal morphological features, including head shape, midpiece defects, and tail irregularities. Feature extraction techniques were employed to quantify key morphological characteristics, which were then used to train the SVM model. The proposed SVM-based approach demonstrated high accuracy in classifying normal versus abnormal sperm morphology, significantly reducing the time and error associated with manual analysis. This method provides an efficient, automated solution for andrology laboratories and fertility clinics, enhancing diagnostic consistency and reliability. By incorporating machine learning techniques, this system holds promise for improving the precision of sperm morphology analysis, ultimately contributing to better fertility treatments and outcomes
XportID: Website for Clustering Indonesian Export Commodities by Destination Continent using Gaussian Mixture Model Lisanthoni, Angela; Trimono, Trimono; Prasetya, Dwi Arman
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27500

Abstract

Exports play a crucial role in driving economic growth and increasing foreign exchange reserves. However, Indonesia's export performance has not yet reached its optimal potential, as evidenced by an 11% decline in export value in 2023. The decrease is partly attributed to the limited range of export destination markets. Therefore, this study aims to analyze export trade patterns to identify the most ideal and potential market locations. The research will employ a quantitative approach, using secondary data from the Central Bureau of Statistics and the 2022 BACI dataset, focusing on the top 5 HS2 commodity types by highest export quantity. Clustering analysis is applied to group markets based on similar characteristics, identifying countries with high, medium, and low export potential for Indonesia’s export strategy. The research develops a website-based clustering system called XportID, utilizing a Gaussian Mixture Model (GMM) with the Expectation-Maximization (EM) algorithm to determine optimal cluster parameters. GMM is preferred for its flexibility and probabilistic system, providing more accurate results compared to distance-based methods. There will be 3, 4, and 5 clusters formed and then the best cluster will be selected by comparing the silhouette score obtained. Results show that the Asian continent has 5 clusters with the best value of 0.7035, the American continent has 3 clusters with the best value of 0.8165, the African continent has 3 clusters with the best value of 0.8534, the Australian continent has 3 clusters with the best value of 0.8540, and the European continent has 4 clusters with the best value of 0.8654. Overall results, the clustering system is categorized as strong structure with average value of 0.8185. Countries with high export potential include Malaysia, Philippines, South Korea, Brazil, Mexico, New Zealand, and Spain. Specifically in Africa, commodities related to HS2-15 show potential for growth.
Co-Authors ', Nachrowie ., Humaidi A. A. Ngurah Gunawan Aan Nehru Awanto Achmad Junaidi Aditya, Wigananda Firdaus Putra Agustina, Fadlila Akio Kitagawa Alam, Fajar Indra Nur Ali, Munawar Amrullah, Ahmad Wildan Andre Leto Andrew Arjunanda Yasin Anggraini Puspita Sari Anindha Lazuardi Aries Boedi Setiawan Arifani, Kahpi Baiquni Arifuddin, Rahman Arum Puspita Ayu Atiana Sofia Kaci Awang, Wan Suryani Wan Azizah, Alisa Jihan Aziziyah, Luqna Baidowi Baidowi Baidowi Baidowi Bambang Nurdewanto Barus, Indra Basitha F Hidayatulail Cahyani Kuswardhani, Hajjar Ayu cahyono, wahyu eko Candra Laksana Damai Arbaus, Damai Damaliana, Aviolla Terza Danang - Destiawan Danang Destiawan Desyderius Minggu Dicky Kurniawan Diyasa, I Gede Susrama Mas Dody Pintarko Dwi Agung Ayubi E, Nachrowie Ekawati, Anies Eko Wahyu Prasetyo Elta Sonalitha Sonalitha Erik Roma Hurmuzi Fahrudin, Tresna Maulana Farhans, Muhammad Izzudin Febriyanti, Alvi Yuana Firdaus Firdaus Firza Prima Aditiawan Gatut Yulisusianto Halim, Christina Hari Fitria Windi Hendry Yudha Pratama Hesti Sholikah, Hesti Hidayatulail, Basitha F Hindrayani, Kartika Maulida Hiroshi Suzuki Hurmuzi, Erik Roma Ibrahim, Mohd Zamri Bin Iffadah, Adhisa Shilfadianis Indra Barus Irsyadi, Muhamad Haidir Ismail, Jefri Abdurrozak Januar, Teddy Jariyah Jeki Saputra Junita Junita Kartika Maulida Hindrayani Kassim, Anuar bin Mohamed Kholid, Fajar Kukuh Yudhistiro, Kukuh Kurniawan, Dicky Kusuma, Dwi Febri Chandra Kusuma, Firdaus Miftakh Kuswardana, Dendy Arizki Laksana, Candra Lestari, Amanda Ayu Dewi Lisanthoni, Angela Maldini, Andry Syva Mas Diyasa, I Gede Susrama Maulidiyyah, Nova Auliyatul Mohammad Ansori Mohammad, Bawazir Fadhil Muhaimin, Amri Muhammad Ansori Muhammad Muharrom Al Haromainy Mulyadi Mulyadi Nachrowie Nachrowie Nachrowie, Nachrowie Nambo Hidetaka Ningrum, Imelda Widya Ninik Sisharini Ninis Herawati Norma Windiyanti Novita Anggraini Nur Rachman Nur Rachman Supatmana Muda Nur Rochman Nur Rochman Nurhalizah, Cesaria Deby Prakoso, Akbar Tri Puput Dani Prasetyo Adi Putri, Irma Amanda Rabi, Abd. Rahman Arifuddin Rahmanda Putri, Endin Rahmawati, Adinda Aulia Respati Respati Rosariawari, Firra Rudi Wilson Sagita Rochman Salim, Hotimah Masdan Santika, Surya Saputra, Wahyu Syaifullah Jauharis Sari, Andina Paramita Siswanto Siswanto Siti Nuurlaily Rukmana, Siti Nuurlaily Stanislaus Yoseph Subairi Subairi Sumartono Sumartono Sumartono Suprayogi Suprayogi Suprayogi Suprayogi Surya Nanda Santika, Surya Takahiro Kitajima Takashi Yasuno Tresna Maulana Fahrudin Trimono Trimono, Trimono Wahyu Dirgantara Wahyuni, Dinar H S wangge, ferdinandus Weisrawei, Yosef Yasin, Andrew Arjunanda Yohanes U D Sipul Yosef Weisrawei Yosua Satria Bara Harmoni Yunia Dwie Nurchayanie Yusaq Tomo Ardianto