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All Journal Jurnal Teknologi dan Manajemen Informatika Sistemasi: Jurnal Sistem Informasi Briliant: Jurnal Riset dan Konseptual Journal on Information Systems, Technology of Information and Communications (JoISTIC) INOVTEK Polbeng - Seri Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas J-SAKTI (Jurnal Sains Komputer dan Informatika) Building of Informatics, Technology and Science bit-Tech Jurnal Informasi dan Teknologi Idealis : Indonesia Journal Information System Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat Abdi Putra Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Prosiding SENTIMETER : Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer
Alun Sujjada
Universitas Nusa Putra
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Public Health

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Perbandingan Rest Api Menggunakan Node Js Dan Php Pada Aplikasi Pemilihan Umum Haryadi, Habbyan Lazuard; Sujjada, Alun; Simatupang, Dwi Sartika
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i2.631

Abstract

This study aims to analyze the comparison of the Representational State Transfer (REST) Application Programming Interface (API) between PHP and Node.js in the context of general election applications. The Prototype System Development Life Cycle (SDLC) method is used for application development. The performance of both programming languages is evaluated based on response speed, ease of development, and system capabilities. Data on Permanent Residents of Malang City with a total of 600 thousand data is used as a sample for database and server testing. The comparison results show that Node.js has a better response speed than PHP. However, PHP has an advantage in terms of ease of development. Both are able to handle applications with a large number of voters based on system capabilities. This research provides insight into the performance of PHP and Node.js in the context of REST API development for election applications
Analisis Clustering Data Penyandang Disabilitas Menggunakan Metode Agglomerative Hierarchical Clustering dan K-means Sujjada, Alun; Insany, Gina Purnama; Noer, Silvia
Jurnal Teknologi dan Manajemen Informatika Vol. 10 No. 1 (2024): Juni 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v10i1.10654

Abstract

Disability issues are still a major concern in society due to the discrimination often faced by people with disabilities. Many of them have abilities that are equal to individuals without physical limitations. Through this case study, this research aims to Cluster disability data by considering three types of disabilities: physical, visual and hearing, and hearing and speech using agglomerative hierachical Clustering and kmeans methods. This research was conducted by analyzing data from people with disabilities in 7 provinces in Indonesia. K-means to group data and agglomerative hierarchical Clustering as a centroid determinant in k-means. to enrich the results of data analysis, the EDA (Exploratory Data Analysis) process is used to identify outliers and anomalies. The results of the data analysis show that there are three main Clusters. The first Cluster has a high level of disability and includes 62 cities and districts, the second Cluster has a medium level of disability with 37 cities and districts, and the third Cluster has a low level of disability with 27 cities and districts. The best evaluation using the Davies Bouldin Index method resulted in two Clusters, indicating a better quality of Cluster division. The results of this study provide a better understanding of the distribution of disability in Indonesia, which can be used as a foundation to improve inclusion and accessibility for people with disabilities. Further recommendations can be made based on these findings to improve their situation in terms of employment and education.
SISTEM PENGATUR SUHU KELEMBABAN RUANGAN PADA BUDIDAYA JAMUR TIRAM BERBASIS ARDUINO Mansur Ponimat; Sujjada, Alun
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 6 No. 2 : Tahun 2021
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/jtiust.v6i2.1548

Abstract

One of the businesses that is often in demand by farmers is cultivating oyster mushrooms because oyster mushrooms are very tasty and have many health benefits, but cultivating oyster mushrooms requires quite complicated care, if the temperature and humidity of the environment where the mushrooms grow are abnormal, it will inhibit the growth of the fungus. With this research the author aims to create a system that can regulate and monitor temperature in real time so that fungal growth will be more fertile and increase production. The development method used to make this research is the waterfall method, this method is very easy to use and is able to solve problems quickly. The main purpose of this research is to create a system that can assist oyster mushroom farmers in regulating and monitoring temperature conditions by using a fan and a water pump as a medium for cooling the temperature which is regulated by an Arduino microcontroller.
SISTEM PENGATUR SUHU KELEMBABAN RUANGAN PADA BUDIDAYA JAMUR TIRAM BERBASIS ARDUINO Mansur Ponimat; Sujjada, Alun
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 6 No. 2 : Tahun 2021
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (500.711 KB) | DOI: 10.54367/jtiust.v6i2.1548

Abstract

One of the businesses that is often in demand by farmers is cultivating oyster mushrooms because oyster mushrooms are very tasty and have many health benefits, but cultivating oyster mushrooms requires quite complicated care, if the temperature and humidity of the environment where the mushrooms grow are abnormal, it will inhibit the growth of the fungus. With this research the author aims to create a system that can regulate and monitor temperature in real time so that fungal growth will be more fertile and increase production. The development method used to make this research is the waterfall method, this method is very easy to use and is able to solve problems quickly. The main purpose of this research is to create a system that can assist oyster mushroom farmers in regulating and monitoring temperature conditions by using a fan and a water pump as a medium for cooling the temperature which is regulated by an Arduino microcontroller.
Application of YOLOv8 Model for Early Detection of Diseases in Bean Leaves Yustiana, Indra; Sujjada, Alun; Tirawati
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2514

Abstract

Bean plant is one of the high economic value horticultural commodities widely cultivated in Indonesia. However, its productivity declines due to pest attacks and leaf diseases. Farmers' limitations in accurately identifying disease types also pose obstacles in early mitigation efforts. Therefore, technology-based solutions capable of quickly and accurately detecting plant diseases are needed. This research aims to develop and evaluate the performance of a leaf disease detection model for bean plants using the You Only Look Once version 8 (YOLOv8) algorithm with a transfer learning approach. The dataset used consists of 1,037 images of bean leaves, classified into three categories: angular leaf spots, leaf rust, and healthy leaves. Data were obtained from two sources, namely field documentation in Sindang Village, Sukabumi Regency, and an open repository on GitHub. The dataset was divided into training data (70%), validation (20%), and testing (10%). The model was trained using the YOLOv8s architecture for 30 epochs and achieved a detection accuracy of 85%. Performance evaluation was conducted using precision, recall, and mean average precision (mAP) metrics. The results of this study are expected to be an initial contribution to the application of artificial intelligence in agriculture, particularly in helping farmers efficiently detect leaf diseases in beans to improve productivity and quality of harvest.
Implementation of Content-Based Filtering in a Novel Recommendation System to Enhance User Experience Sanjaya, Imam; Sujjada, Alun; Pratama, Yudistira
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2833

Abstract

This study addresses a critical challenge in digital novel platforms: the difficulty of delivering personalized and accurate recommendations due to limited user interaction data. This limitation often leads to irrelevant or generic suggestions, which can diminish user engagement and hinder content discovery. The significance of solving this issue lies in enhancing user experience by ensuring that readers are presented with novels that truly align with their interests, even in the absence of extensive behavioral data. To overcome this problem, the study proposes an innovative hybrid recommendation system that integrates Content-Based Filtering (CBF) with the Random Forest algorithm. The system generates personalized recommendations by analyzing novel attributes such as title, genre, score, and popularity. The methodology involves extracting features from textual data using Term Frequency-Inverse Document Frequency (TF-IDF), followed by the calculation of cosine similarity to assess title relevance. These similarity scores are then combined with popularity predictions derived from the Random Forest model to produce final recommendations that reflect both content similarity and statistical relevance. The proposed system demonstrates strong performance, achieving an accuracy of 94.0%, precision of 81.4%, recall of 80.3%, and an F1-score of 80.8%. These results underscore the system’s capability to deliver accurate and diverse suggestions. By enhancing personalization and addressing the limitations of conventional CBF systems, this hybrid approach offers practical value for digital novel platforms. It serves as an effective tool for improving content discovery, increasing reader satisfaction, and supporting user retention in content-rich environments.
Sistem Prediksi Konsumsi Energi Listrik Subsidi dan Non Subsidi Berbasis Web dengan Metode RNN (Kasus Kota Sukabumi) Insany, Gina Purnama; Sujjada, Alun; Lidena, Salwa Dwi; Fadilah, Muhammad Sahrul; Wilianti, Refi
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.917

Abstract

Sukabumi City experiences an annual increase in electricity consumption, especially in subsidized and non-subsidized categories. However, the energy distribution planning process remains manual and reactive. This research developed a web-based electricity consumption prediction system using the Recurrent Neural Network (RNN) method integrated with the Laravel framework. The development process applied the Rapid Application Development (RAD) method and system modeling using UML. The RNN model achieved a prediction accuracy of 92.4% with an MAE of 12.38 kWh and RMSE of 16.12 kWh. The application provides interactive prediction visualizations to support more efficient energy planning.
Melangkah ke Masa Depan Literasi Digital: Rancang Bangun Sistem Genusian Course Academy dengan Pendekatan Hybrid Collaborative Filtering dan Content-Based Filtering Firdaos, Helfi Apriliyandi; Sujjada, Alun; Somantri, Somantri
BRILIANT: Jurnal Riset dan Konseptual Vol 10 No 2 (2025): Volume 10 Nomor 2, Mei 2025
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/briliant.v10i2.1957

Abstract

The recommendation system is an important component in enhancing the user experience by providing relevant and personalized course recommendations that align with individual preferences and needs. The Hybrid Collaborative Filtering approach combines the Collaborative Filtering (CF) method, which analyzes user interaction patterns, with the Content-Based Filtering (CBF) method, which evaluates the similarity of course content features. Implementing this hybrid system is expected to overcome the limitations of each method, such as the cold start problem in CF and the limitation of recommendation variety in CBF. This research aims to design a recommendation system in the academic environment known as “Genusian Course Academy”. This hybrid approach is expected to overcome the weaknesses of each approach and result in a more accurate and personalized recommendation system. The implementation of this system is expected to improve the online learning experience and help users find training that matches their needs users.
Pengembangan Sistem Irigasi Tetes Non Circulation Menggunakan Iot dan Analisis Big Data Mustopa, Ato; Sujjada, Alun; Kharisma, Ivana Lucia
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 13, No 1 (2024): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v13i1.5388

Abstract

Untuk mengatasi pemborosan air pada tanaman tomat, salah satu solusinya yang bisa digunakan adalah dengan memanfaatkan teknologi Internet of Things (IoT) dan analisis big data. Sistem irigasi tetes non-circulation sangat efisien dalam mengurangi penggunaan air yang berlebihan. IoT dapat membantu petani dalam memantau dan mengendalikan irigasi secara akurat melalui bantuan sensor kelembaban tanah dan nutrisi yang terhubung dengan internet. Data yang dikumpulkan dianalisis secara real-time, Sehingga kebutuhan air dapat diatur sesuai keinginan. Teknologi IoT juga dapat membantu mengontrol sistem irigasi melalui website, sehingga dapat meningkatkan efisiensi waktu dan tenaga. Penelitian ini menggunakan konsep big data untuk mengelola dan menganalisis data irigasi. Data sensor disimpan dalam database online yang dapat diakses petani melalui website. Dengan demikian, petani dapat memantau dan mengatur nutrisi dan air tanaman secara real-time. Dengan penerapan IoT dan analisis big data, penggunaan air dalam irigasi tetes non-circulation pada tanaman tomat hidroponik dapat dioptimalkan, meningkatkan efisiensi, dan mencapai pertanian yang berkelanjutan.
Ear Biometric Identification based on Gabor Filters using Backpropagation Neural Networks Kumaran, Ivano; Yudono, Muchtar Ali Setyo; Sujjada, Alun
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4573

Abstract

The development of reliable security systems is crucial for protecting personal information and access control. Ear biometrics, which utilizes the unique structure of the ear, is a promising method for human identification due to its resistance to forgery. This research aims to design and test an ear biometric identification system using images of the right ear without accessories from five men, totaling 224 images. The preprocessing steps include resizing the images, converting them to grayscale, and applying Gaussian filters. Image segmentation is performed using Canny edge detection, followed by morphological operations such as dilation and hole filling. Features of the ear images are extracted using Gabor filters, and classification is carried out using Backpropagation Neural Networks. The system achieved an average success rate of 88.8% across five testing scenarios, with the highest accuracy of 94% in the first and fifth scenarios. Sensitivity for classes 1, 2, 3, 4, and 5 was 98%, 74%, 92%, 96%, and 82%, respectively. Specificity reached 100% for classes 1 and 3, and 94%, 97.5%, and 94.5% for classes 2, 4, and 5. Based on the results of accuracy, sensitivity, and specificity testing, the ear biometric system using Gabor feature extraction and Backpropagation Neural Network classification demonstrates good performance and potential for security applications.
Co-Authors adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Aden Rahmat Ramdani Alpariji, Salman Alvira Fauziah Rahmah Alvira Fauziah Rahmah Dwi Sartika Simatupang Erlinda Juniar Esa Nurfaizah Fadilah, Muhammad Sahrul Fadillah Alviqih falentino sembiring Febriansyah Febriansyah Firdaos, Helfi Apriliyandi Haryadi, Habbyan Lazuard Hasanah, Nurul Islam Ihsan Nurhuda Indra Yustiana Insany, Gina Purnama Irpanudin Jelita Asian Juniar, Erlinda Kamdan Khadijah Kibtiyah Kharisma, Ivana Lucia Kumaran, Ivano Lidena, Salwa Dwi Mansur Ponimat Mayang Gunawan Mega Putri Utami Meinanda, Muhamad Dani Mohamad Salman Farizi Muchtar Ali Setyo Yudono Muhamad Ridwan Nullah Muhammad Ikhsan Thohir Mustopa, Ato Noer, Silvia Panji Pratama Pratama, Yudistira Putra, Wahyu Irwan Reka Reni Andian Reni Nur Anggraeni Rizal Fadli Rizaldi, Fazrin Muhammad Rizki Maulana Sanjaya, Imam Sebayang, Alvin Christoper Sepriyadi, Adi Somantri Somantri Somantri, Somantri Sulaeman, Asep Surahman Syam, Fajar M Tirawati Usnul Latipah Vemi Januarita Putri Wilianti, Refi Yunita Ramanda Yustiana, Indra Zaenal Alamsyah Zulia nur permatasari