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All Journal Jurnal Terapan Abdimas Kurawal - Jurnal Teknologi, Informasi dan Industri JATI (Jurnal Mahasiswa Teknik Informatika) Rainstek : Jurnal Terapan Sains dan Teknologi Jurnal Pengabdian Masyarakat Bhinneka BIMASAKTI
Nugraha, Danang Aditya
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PENGEMBANGAN BAHAN PELAJARAN KOMPUTER DAN JARINGAN DASAR KELAS IX DI SMP ISLAM DRUJU SUMBERMANJING WETAN Afandi, Fajar; Nugraha, Danang Aditya; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 6 No 1 (2023): BIMASAKTI
Publisher : Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v6i1.9350

Abstract

Metodologi pengembangan yang diterapkan dalam penelitian dan telah dimodifikasi dengan tahapan penelitian yang sudah dirancang (RnD) dengan bebrapa modifikasi dalam pengembngan bahan modul  dibagi menjadi 10 tahap, yaitu: (1) permasalahan dan potensi, (2) pengumpulan data, (3) perkembangan produk, (4) validasi ahli guru dan ahli siswa , (5) evaluasi produk, (6) Uji coba kelompok kecil, (7) evaluasi produk, ( 8) pengujian kelompok besar, (9) evaluasi produk, (10) pembuatan produk. Hasil pengembangan modul  dasar yang dikembangkan di komputer dan terhubung ke jaringan sesuai modul Rnd dibangun dan diverifikasi oleh beberapa ahli, yaitu:(1) ahli Guru mencapai 94,1%, (2) ahli Siswa mencapai 93,54%, (3) kelompok esai kecil mencapai 80,4%, (4) kelompok esai kelompok esai mencapai 91,96% dan nilai rata-rata bilai sebesar 90,79%. Berdasarkan dari hasil evaluasi dengan dikatakan bahwa modul pemebelajaran  dasar computer dan jaringan dapat digunakan dalam proses pembelajaran di SMP Islam Druju khususnya mata pelajaran komputer dasar kelas IX komputer dan jaringan program komputer.
PROTOTYPE SISTEM NOTIFIKASI MONITORING DAYA LISTRIK DENGAN METODE FUZZY BERBASIS ANDROID Budianto, Alfa Wahyu; Tri Sulistiyo, Muhammad Priyono; Nugraha, Danang Aditya
Jurnal Fakultas Teknologi Informasi Vol 7 No 2 (2025): BIMASKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v7i2.10783

Abstract

Increasing electrical energy consumption in the household sector is a major concern in effective energy management to prevent waste and reduce costs. Conventional electrical power monitoring systems have weaknesses in terms of time and efficiency because they require manual calculations of the use of each electronic device. The development of mobile technology and the Internet of Things (IoT) offers new opportunities in developing monitoring systems that are more interactive and can be accessed in real-time via Android devices. However, the biggest challenge is presenting power usage information that is easy for users to understand. Fuzzy methods offer a solution by converting numerical data into simpler information such as "high", "medium" or "low" power consumption categories, making it easier for users to understand. In addition, the fuzzy method can also provide warning notifications when power consumption exceeds a certain limit. This research aims to develop an Android-based electrical power monitoring system using the fuzzy method. This system is designed to provide direct and efficient power consumption information, as well as notifications that help users monitor household energy consumption. It is hoped that this system can contribute to better energy management, provide solutions to excessive energy consumption, and become the basis for the development of smart home technology in Indonesia.
ANALISIS PERBANDINGAN KINERJA YOLO DAN CAMSHIFT DALAM PELACAKAN OBJEK BERBASIS VIDEO Miharja, Gangga Prakarsa; Nugraha, Danang Aditya; Abdul Aziz
Jurnal Fakultas Teknologi Informasi Vol 7 No 2 (2025): BIMASKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v7i2.10947

Abstract

This study aims to implement and analyze the YOLO (You Only Look Once) method for human object tracking and compare it with the Camshift (Continuously Adaptive Mean Shift) method. Supported by the rapid development of artificial intelligence (AI) in information technology, this research explores the capabilities of both methods in object tracking. The process involves video capture, tracking using YOLO and Camshift, and performance evaluation through the Intersection over Union (IoU) metric under various lighting conditions. Both methods are integrated into an Arduino-based tracking device. The results show that YOLO outperforms in environments with complex backgrounds and optimal lighting, although it is slower, while Camshift is faster but less accurate under varying lighting conditions. Both methods are effective in monitoring human movement, but there is a trade-off between accuracy and speed. With superior efficiency and accuracy, YOLO is more suitable for real-time human object tracking. Future research is suggested to combine or enhance detection and tracking algorithms, optimize the system with advanced hardware, test under real-world conditions, and explore the integration of other technologies to create a more reliable and adaptive tracking system.
PEMILIHAN PROTOKOL VIRTUAL PRIVATE NETWORK MENGGUNAKAN MIKROTIK UNTUK KEBUTUHAN AKSES JARAK JAUH PADA SMK NEGERI 11 MALANG Ramadhanti, Rizka Laela Saputri; Zaini, Ahkmad; Nugraha, Danang Aditya
Jurnal Fakultas Teknologi Informasi Vol 8 No 1 (2025): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i1.12616

Abstract

State Vocational School 11 Malang is a school that uses technology and information. Administrators have the duty to monitor and understand the condition of the school network. The problem when the administrator is outside the local network or public network. The administrator needs remote access to access the server or application safely without data leakage. VPN provides security by encrypting internet traffic. PPTP, L2TP, OpenVPN are VPN protocol options that can be used to connect between different networks. In this research, testing was carried out between these protocols to find out which one has the best performance and the ability to maintain the confidentiality of the data and information stored in it.
PEMILIHAN PROTOKOL VIRTUAL PRIVATE NETWORK MENGGUNAKAN MIKROTIK UNTUK KEBUTUHAN AKSES JARAK JAUH PADA SMK NEGERI 11 MALANG Ramadhanti, Rizka Laela Saputri; Zaini, Akhmad; Nugraha, Danang Aditya
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.12615

Abstract

State Vocational School 11 Malang is a school that uses technology and information. Administrators have the duty to monitor and understand the condition of the school network. The problem when the administrator is outside the local network or public network. The administrator needs remote access to access the server or application safely without data leakage. VPN provides security by encrypting internet traffic. PPTP, L2TP, OpenVPN are VPN protocol options that can be used to connect between different networks. In this research, testing was carried out between these protocols to find out which one has the best performance and the ability to maintain the confidentiality of the data and information stored in it.
PENERAPAN DATA MINING UNTUK MENGKLASIFIKASI PENERIMA BANTUAN PROGRAM KELUARGA HARAPAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Jesika, Amelia; Budianto, Alexius Endy; Nugraha, Danang Aditya
Jurnal Fakultas Teknologi Informasi Vol 8 No 1 (2025): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i1.12826

Abstract

The Family Hope Program (PKH) is a governmental initiative in Indonesia designed to decrease poverty and improve the welfare of families. However, the process of identifying eligible families frequently encounters difficulties. To address this, the study applies data mining techniques with the Support Vector Machine (SVM) method to classify prospective PKH recipients in Bangka Leleng Village. The research utilizes 1,039 data samples of recipients from 2019 to 2023, based on five key attributes: age, income, number of dependents, occupation, and home ownership status. Data processing was conducted using Python in the Google Colab environment. The research workflow involved data collection, preprocessing, splitting data for training and testing, analysis, and evaluation using a Confusion Matrix. The test results indicated that the SVM method is highly effective in classifying PKH recipients, achieving an accuracy rate of up to 96%. This optimal accuracy was obtained by employing the RBF kernel, which demonstrated superior performance compared to other kernels. It is anticipated that this research will provide a more efficient and transparent method for determining aid recipients, leading to a more precise distribution of assistance.
OPTIMASI RANDOM FOREST TERHADAP DATA PENYAKIT LIVER MENGGUNAKAN FIREFLYALGORITHM Sunarjo, Nemesius; Nugraha, Danang Aditya; Santoso, Heri
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.13041

Abstract

Liver disease is one of the most dangerous diseases for human survival. In an effort to find out liver disease early on, a classification method is needed. Researchers conducted testing and classification of lliver disease with the Random Forest algorithm which was then optimized with the Firefly algorithm. The purpose of this study is to learn how the application of the firefly algorithm in optimizing the accuracy of the random forest algorithm in liver disease. The data used is 1700 data with 11 attributes. The findings of this study with the Random Forest algorithm produced an accuracy of 87.24% while when optimized using the Firefly Algorithm produced an accuracy of 93.24%. The findings demonstrated a rise in the precision of the Random Forest algorithm and optimized using Firefly Algorithm.
PENERAPAN ALGORITMA LOGISTIC REGRESSION UNTUK KLASIFIKASI PENYAKIT STROKE Amelia, Rachel Trivica; Nugraha, Danang Aditya; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.13201

Abstract

Stroke is one of the leading causes of death worldwide, ranking after heart disease and cancer. Early detection of stroke risk is essential to enable faster and more accurate treatment. The purpose of this study is to apply the Logistic Regression algorithm to classify stroke cases based on several risk factors, including gender, age, hypertension, heart disease, marital status, occupation, residence type, average glucose level, body mass index (BMI), smoking status, and stroke status. The dataset used in this research was obtained from Kaggle and consists of 5,110 patient records. The research process involves several stages, including data cleaning, data transformation, and normalization using the Min-Max Scaler method, followed by splitting the data into training and testing sets with various proportions (90%-10%, 85%-15%, 80%-20%, 70%-30%, and 65%-35%). The evaluation was conducted using a Confusion Matrix with performance metrics such as accuracy, precision, recall, and F1-score. The analysis results show that the 90%-10% data split achieved the highest accuracy of 76.17%, with precision and recall values indicating that the model performs well in identifying non-stroke cases. However, performance on the minority class (stroke) remains relatively low, suggesting the need for improvement through data imbalance handling. Overall, the application of the Logistic Regression algorithm proved to be effective for initial stroke classification, although accuracy can still be improved through resampling techniques or advanced model optimization.
Co-Authors Abdul Aziz Abdul Aziz Afandi, Fajar Ahsan, Moh Akhmad Zaini, Akhmad Alvionita, Modesta Noviani Amanda, Kharisma Amelia, Rachel Trivica Anggri Sartika Wiguna Budianto, Alexius Endy Budianto, Alfa Wahyu Cahyani, Indah Ayu Djapalata, Anggreni Shinta Harianto, Wahyudi Heri Santoso Jesika, Amelia Luriansyah, Rizky Putra Maharani, Delvi Anjarita Miharja, Gangga Prakarsa Moh Ahsan Nage, Vensislaus Ramadhanti, Rizka Laela Saputri Sudiyono Sudiyono Sunarjo, Nemesius Tri Sulistiyo, Muhammad Priyono Zaini, Ahkmad