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Infotek : Jurnal Informatika dan Teknologi
Published by Universitas Hamzanwadi
ISSN : 26148773     EISSN : 26148773     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
INFOTEK Jurnal Informatika dan Teknologi Fakultas Teknik Universitas Hamzanwadi selanjutnya disebut Jurnal Infotek (e-ISSN: 2614-8773) merupakan Jurnal yang dikelola oleh Fakultas Teknik Universitas Hamzanwadi yang mempublikasikan artikel ilmiah hasil penelitian atau kajian teoritis (invited authors) dalam bidang (1) keilmuan informatika, (2) Rekayasa Perangkat Lunak, (3) Multimedia, (4) Jaringan Komputer, (5) Data Mining, (6) Image Processing, (7) Komputer Vision, (8) Mikrokontroller, (9) Robotik, (10) IOT yang belum pernah dipublikasikan. Jurnal Infotek diterbitkan oleh Fakultas Teknik Universitas Hamzanwadi dua kali setahun yaitu pada bulan Januari dan Juli. Jurnal Infotek Telah Terindeks pada Google Scholar.
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 388 Documents
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Rekayasa SmartHome System Berbasis Internet of Things Zulkarnaen, Muhammad Fauzi; Aliy Nauval Hanafi; Mohammad Taufan Asri Zaen
Infotek: Jurnal Informatika dan Teknologi Vol. 7 No. 2 (2024): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v7i2.26545

Abstract

In the current digital era, the Internet of Things (IoT) has emerged as a concept that presents new opportunities to expand sustainable internet connectivity. In the context of Smarthome, IoT enables the control of electronic devices through smartphone applications using internet connectivity, bringing various benefits, including increased comfort, enhanced security, better electricity savings, and introducing innovative solutions that not only allow real-time monitoring of home environments but also provide accessibility by considering the needs and preferences of various users, including families, busy individuals, and people with disabilities. The implementation of an IoT-based Smarthome system utilizes NodeMCU 8266 as a microcontroller, MIT App Inventor as a control tool, Thingspeak as a data processing platform, ESP32-CAM as a camera, and Telegram bot as a notification sender. This system includes the control of lights, fan, environmental monitoring, motion detection, gas leak detection, and fire detection. Various sensors are used, including Flame sensors for fire detection, MQ5 sensors for gas leak detection, and light sensors for automatically turning on lights. Relays are also applied to connect electronic devices to the system. Testing and system analysis show that equipment control operates according to the given commands, provided that the internet connection remains stable and continuous. This underscores the importance of a reliable network infrastructure in supporting the smooth operation of IoT-based Smarthome systems.
Analisis Klasifikasi Konsentrasi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor alwanda, Almi yulistia; Ema Utami; Ainul Yaqin
Infotek: Jurnal Informatika dan Teknologi Vol. 7 No. 2 (2024): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v7i2.27084

Abstract

Hamzanwadi University located in West Nusa Tenggara boasts a Faculty of Engineering, which plays a pivotal role in delivering top-notch higher education. This faculty offers four highly coveted programs—Informatics Engineering, Information Systems, Computer Engineering, and Environmental Engineering—that attract a significant number of students. The increase in enrollment in these programs underscores the faculty's success, particularly owing to the promising job opportunities in these fields. Nevertheless, students often encounter intricate challenges when selecting their area of specialization that resonates with their interests and capabilities. In addressing this concern, the Faculty of Engineering at Hamzanwadi University provides diverse concentration options like Data Science, RPL, and Multimedia. To aid students in making informed decisions regarding their study concentration, this study employs the K-Nearest Neighbor (KNN) algorithm to analyze the classification of student concentrations. This research adopts an experimental approach and utilizes data collection methods such as observation, interviews, and surveys. The dataset comprises seven attributes including NIM, Gender, GPA, Data Science Course Grade, RPL Course Grade, and Multimedia Course Grade, processed using the KNN algorithm through Google Colab. The research outcomes reveal that with k=2 and 8-fold cross-validation, the achieved accuracy stands at 67%.
Pengembangan Sistem Penerjemah Kalimat Bahasa Isyarat Bisindo To Text Dengan Kinect Real Time Aldin Fathiray; Joni Maulindar; Wiji Lestari
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.26116

Abstract

People with hearing and speech impairments often have difficulty communicating with the general public due to a lack of understanding of sign language. This results in social isolation and barriers to accessing information and education. The development of sign language translator technology is expected to improve communication and independence of people with disabilities. The methodology used in this research includes data collection through literature studies, questionnaires, and documentation. The BISINDO data processing algorithm in this research uses the Long short-term memory (LSTM) method to detect skeletons on the hands, face and body. The system implementation uses kinect to capture real-time hand movements. System development uses the Agile method to ensure functionality and fulfillment of user needs. From the evaluation results by testing system performance using confusion matrix by calculating accuracy, recall, precision and F1-Score values. As well as datasets taken in realtime with a total of 90 data, each consisting of 30 actions of my sign language, 30 actions of good sign language, 30 actions of iloveyou sign language. The results show that the system has an accuracy value of 1.0, recall 1.0, precision 1.0 and F-1 Score 1.0 with LSTM algortima, epoch 140 and response data that shows positive towards the system
Pengembangan Website Speech To Video Bahasa Isyarat Indonesia (Bisindo) Berbasis Algoritma Long Shot Term Memory Deleviar, Angky Fay; Intan Oktaviani; Hanifah Permatasari
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.26117

Abstract

Indonesian Sign Language (BISINDO) is an essential communication tool for more than one million deaf people in Indonesia. This research aims to develop a Speech to BISINDO website based on the Long Short-Term Memory (LSTM) algorithm to overcome barriers in communication and learning processes for the deaf community. The research stages include data collection through questionnaires and literature studies to understand user needs, as well as system development using the Rational Unified Process (RUP) method. The system is designed to convert voice input into sign language videos and support the sign language learning process through interactive features. Testing was conducted to ensure that the system meets functional and non-functional needs. The results show that the Speech to BISINDO website is effective in translating speech into sign language videos with high accuracy, as well as supporting better communication and learning between deaf people and the general public. The system offers an innovative solution to improve access to information and learning process for people with hearing impairment.
Penerapan Algoritma Decision Tree Untuk Menentukan Terapi Pada Anak Autis Mahendra, Satria Yudha; Wiji Lestari; Intan Oktaviani
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.26121

Abstract

People with hearing and speech impairments often have difficulty communicating with the general public due to a This research focuses on the development of a classification system for autism levels in children using the Decision Tree algorithm. This system considers various aspects of the psychomotor abilities of autistic children, such as balance, hand coordination, flexibility, and muscle strength. Observation data was taken from autistic children aged 5-8 years who attended SLB Anugerah Colomadu, and was used to build a Decision Tree model. The results showed that this model had 0,9 accuracy in classifying autism rates based on the observed psychomotor features. In addition to classification, the system also provides recommendations for psychomotor therapy that are specific and according to the level of autism that the model has classified. This approach is expected to improve the quality of interventions for autistic children by facilitating the development of their psychomotor skills. With this system, it is hoped that educators and therapists can provide more targeted treatment, so that autistic children can develop more optimally according to their individual needs. This structured and data-based approach is also expected to be a reference in the development of other intervention methods in the future
Perspective Owner Pada Enterprise Architecture Menggunakan Zachman Di Perusahaan Distributor Mesin Rahman, Muhammad Ryo; Andry, Johanes Fernandes; Lee, Francka Sakti
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.27651

Abstract

Machinery distributor companies have an important role in the industrial supply chain, where organizations are faced with various challenges, such as the possibility of stock management which is vulnerable to data errors and operational losses. This can result in operational inefficiencies and hinder the achievement of business goals. Implementing Enterprise Architecture (EA) using the Zachman Framework can play a role in helping overcome these challenges and maximizing the benefits of implementing information systems. This research aims to design an EA blueprint for machine distributor companies using the Zachman Framework mapping method. Data collection was carried out through interviews and observations to analyze the organization's value chain and develop scope, enterprise model and system model with a focus on data, function and network aspects. The results of this research produced a comprehensive and structured EA blueprint design, with a focus on three main aspects, namely data, function and network from the perspective of the owner and designer. This design is expected to help machine distributor companies achieve goals in the stock management process to increase data accuracy and operational efficiency
Penerapan Metode AHP untuk Sistem Pendukung Keputusan Pemilihan Siswa Teladan Puspita, Ari; Hilda Amalia; Ida Faridah; Seni Kurniasari; Yuyun Yuningsih
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.27683

Abstract

Students are an important element in education who are expected to become quality individuals. The selection of exemplary students is important to do, this aims to motivate students to excel, but a system that only relies on academic grades can hinder non-academic development. Therefore, non-academic aspects such as morals and extracurricular activities are also included in the assessment. Decision Support Systems (DSS) help analyze situations with criteria that are not always clear, using the Analytical Hierarchy Process (AHP) method to organize complex problems. This study developed a DSS for selecting exemplary students at SMP Muhammadiyah Kawali with four criteria: attendance, discipline, achievement, and extracurricular. The results showed that exemplary students with the highest weighted scores scored 0.1920. The resulting DSS was able to select exemplary students more objectively and transparently based on existing criteria, namely discipline, attendance, achievement and extracurricular activities. With the implementation of this system, it is able to increase objectivity and fairness in selecting exemplary students and motivate students to achieve better
Model Hibrida K-Nearest Neighbors Berbasis Genethic Algorithm untuk Prediksi Penyakit Ginjal Kronis Rukiastiandari, Sinta; Rohimah, Luthfia; Aprillia, Aprillia; Chodidjah, Chodidjah; Mutia, Fara
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.27918

Abstract

Chronic Kidney Disease, which is often abbreviated as PGK, is a serious disease that is of major concern to society and the medical world. This disease can cause various serious complications if not treated properly and early. Therefore, accurate prediction of CKD is very important to support early intervention that can slow disease progression, prevent further complications, and increase the patient's chances of recovery. This research aims to increase the accuracy of PGK predictions by developing a hybrid model that combines the K-Nearest Neighbors (KNN) algorithm with optimization using the Genetic Algorithm (GA). In this approach, the KNN algorithm is used to build a prediction model, while GA acts as an optimization tool that improves model performance. The effectiveness of the optimized model is evaluated using key metrics such as accuracy, precision, recall, and area under the curve (AUC). The results show a significant increase in performance, with accuracy increasing by 17.75%, precision increasing by 23.84%, and recall increasing by 5.34%. This research makes an important contribution to the development of data mining technology for clinical applications and opens up opportunities for further improvements in the future in increasing the prediction accuracy of chronic diseases such as CKD
Prediksi Diabetes Menggunakan Algoritma K-Nearest (KNN) Teknik SMOTE-ENN Amri, Zaenul; Muhammad Rodi; M. Nurul Wathani; Amir Bagja; Zulkipli
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.27975

Abstract

Nowadays, diabetes is a common disease affecting millions of people worldwide, and it is generally more prevalent among women. Recent health research has adopted various innovative and advanced technologies to diagnose individuals and predict diseases based on clinical data. One such technology is Machine Learning (ML), which enables more accurate diagnosis and prediction. The data used in this study is the Pima Indian women diabetes dataset from Kaggle and the UCI data repository. This study focuses on predicting diabetes using the KNN algorithm model by applying optimization to the dataset using the SMOTE-ENN technique to enhance prediction accuracy for Pima Indian women. The dataset was trained and tested with five different splits using Jupyter Notebook to determine the best accuracy for the KNN algorithm model. Parameters such as classification accuracy, classification error, and the ROC curve were evaluated, along with identifying the variables influencing the risk of diabetes. The results showed that applying SMOTE-ENN optimization to the research dataset significantly improved the prediction accuracy using the KNN algorithm model. With a 70% training and 30% testing data split, the model achieved a classification accuracy of 0.96, a classification error of 0.04, and an AUC of 0.95. These predictions indicated that Pima Indian women are more likely to develop diabetes due to factors such as age above 33 years, the number of pregnancies, excessive sugar consumption, blood pressure, skin thickness, insulin levels, BMI (Body Mass Index), and genetic predisposition to diabetes
Pengelompokan Keaktifan Anggota Perpustakaan Menggunakan Algoritma K-Means Sulaiman, Hamdun; Yuri Yuliani; Kukuh Panggalih; M. Iqbal Alifudin; Kudiantoro Widianto
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.27978

Abstract

This study aims to analyze membership activity at the Rimba Baca Library in South Jakarta using the K-Means algorithm. The background of this research is the library's need to understand membership patterns and improve services based on visit and book borrowing data. The dataset for this study consists of 81 membership records collected from 2023 to 2024. The methodology involved collecting visit and book borrowing data, then applying the K-Means algorithm to cluster members based on their activity levels. The results of the study indicate the presence of three clusters with different characteristics. Cluster 1 comprises very active members, while Clusters 0 and 2 exhibit lower levels of activity. These findings provide insights for the library to develop more effective service strategies, such as special promotions and programs to increase activity among less active member groups. Additionally, the study shows that membership types allowing for more book borrowings do not necessarily correlate with high activity levels. With this information, the library can enhance member engagement and optimize the use of existing resources, thereby creating a more dynamic and interactive environment for all visitors

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