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Teknik: Jurnal Ilmu Teknik dan Informatika
Published by Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern
ISSN : 28088751     EISSN : 27982513     DOI : 10.51903
Core Subject : Science,
Computer Science & IT
Jurnal Ilmu Teknik dan Informatika (TEKNIK) menerbitkan satu-satunya makalah yang secara ketat mengikuti pedoman dan template TEKNIK untuk persiapan naskah. Semua manuskrip yang dikirimkan akan melalui proses peer review double-blind. Makalah tersebut dibaca oleh anggota redaksi (sesuai bidang spesialisasi) dan akan disaring oleh Redaktur Pelaksana untuk memenuhi kriteria yang diperlukan untuk publikasi TEKNIK. Naskah akan dikirim ke dua reviewer berdasarkan pengalaman historis mereka dalam mereview naskah atau berdasarkan bidang spesialisasi mereka. TEKNIK telah meninjau formulir untuk menjaga item yang sama ditinjau oleh dua pengulas. Kemudian dewan redaksi membuat keputusan atas komentar atau saran pengulas. Reviewer memberikan penilaian atas orisinalitas, kejelasan penyajian, kontribusi pada bidang/ilmu pengetahuan. Jurnal ini menerbitkan artikel penelitian (research article), artikel telaah/studi literatur (review article/literature review), laporan kasus (case report) dan artikel konsep atau kebijakan (concept/policy article), di semua bidang : Network Computer and Security Computer Architecture Design Data Mining Human Computer Interaction Sistem pakar (Expert System) Jaringan syaraf tiruan (Artificial Neural Network) Algoritma genetic. Penalaran komputer berbasis kasus (Case Based Reasoning) Agen Cerdas (Intelligent Software Agents) Geographical Information System
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 95 Documents
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Peningkatan Proses Pengolahan Limbah Sewage Treatment Plant dalam Pencapaian Baku Mutu Air Limbah Dengan Metode Six Sigma di PT. XY Ririn Mulyani; Alfiya Rokhmah; Fajar Anzari
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.702

Abstract

. PT. XY is a company engaged in the printing sector, especially in the security products sector, which is required to process its wastewater so that it can meet the required wastewater quality standards, especially in the processing of domestic waste originating from human waste and other consumption waste such as washing water, rinsing and so on. A Sewage Treatment Plant is a domestic liquid waste processing installation to process residual domestic liquid waste so that it becomes clear and is no longer harmful to the environment. The problem that arises is one of the measurement parameters in the wastewater quality standard, namely total coliform (coliform bacteria), which has not met the requirements for several months. This study aims to process domestic waste in the sewage treatment plant process with the six-sigma method to achieve results according to the requirements of the liquid waste quality standard. It is safe to be discharged into water bodies. The results of this study from 6 parameters 5 parameters meet the wastewater quality standards, namely: pH, Total Suspended Solids / TSS, Biochemical Oxygen Demand5 / BOD5, Chemical Oxygen Demand / COD, Oil and fat, while the average level of Total Coliform in a year is 6,448 MPN / 100 ml, not yet meeting the wastewater quality standards of 3,000 MPN / 100 ml. The results of the six-sigma calculation found a result of 1.5 Sigma (σ) where the process is in the non-competitive category
Penerapan Algoritma Klasifikasi untuk Deteksi Dini Penyakit Jantung Koroner Berdasarkan Gejala Klinis Setiawan, Dita; Ali Muhammad; Siti Herawati Fransiska Dewi
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.706

Abstract

Coronary heart disease (CHD) remains a leading cause of mortality worldwide. Early detection is essential to reduce complications and improve patient outcomes. This study aims to develop a classification model using machine learning algorithms to predict CHD risk based on clinical symptoms. The dataset used is the Cleveland Heart Disease dataset from the UCI Machine Learning Repository, consisting of 303 patient records with 14 clinical features. The preprocessing stage involved handling missing values, normalizing features, and transforming categorical variables. Four classification algorithms were applied: K-Nearest Neighbors (K-NN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Each model was trained using stratified 10-fold cross-validation to ensure generalizability. Evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics showed that the Random Forest algorithm achieved the highest performance with 87.2% accuracy. Feature importance analysis indicated that chest pain type, resting blood pressure, cholesterol, and ST depression were the most influential indicators. These results demonstrate that machine learning, particularly Random Forest, can effectively support early diagnosis of CHD in clinical settings and has the potential to be integrated into clinical decision support systems (CDSS).
Perancangan Sistem Monitoring Kehadiran Karyawan Menggunakan Webcam dan Algoritma Eigenface Studi Kasus di PT Bluepoll Rusito; Suprapti; Yuli Fitrianto
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.714

Abstract

Facial recognition, a branch of image processing, is widely used in attendance systems to improve efficiency and security. This study develops an employee attendance monitoring system that integrates facial recognition using the Eigenface algorithm in OpenCV. The system records each individual's facial data alongside a password, enabling automated attendance tracking. Testing results indicate that with a database of 10 facial entries, the system achieved 100% accuracy in recognizing individuals. However, as the database expanded beyond 10 entries, accuracy declined to 80%, influenced by factors such as lighting variations, differences in facial structures, and increased data volume. This study employed a Research and Development (R&D) methodology, with expert validation yielding a score of 3.4, categorizing the system as "Highly Valid." User testing with 11 participants resulted in an overall score of 36, classifying the system as "Very Good (Valid)." The findings highlight the potential of facial recognition in improving attendance management while minimizing fraudulent entries. Future research should focus on optimizing recognition accuracy in larger databases through refined preprocessing techniques, image quality adjustments, and deep learning models.
Evaluasi Performa Algoritma Klasifikasi dalam Prediksi Kekambuhan Kanker Tiroid Pasca Terapi RAI: Studi Kasus Dataset RAI Therapy Wahyu Nugraha; Raja Sabaruddin
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.717

Abstract

Thyroid cancer is the most common endocrine malignancy, with a steadily increasing incidence rate. Although the overall survival rate is relatively high, the risk of recurrence after definitive treatment such as Radioactive Iodine (RAI) therapy remains a significant clinical challenge. Predicting recurrence risk is crucial for optimizing monitoring strategies and interventions. With advances in technology, machine learning (ML) approaches are increasingly utilized to support medical predictions, including the recurrence of thyroid cancer. This study aims to evaluate the performance of four classification algorithms—Logistic Regression, XGBClassifier, Random Forest Classifier, and Voting Classifier—in predicting thyroid cancer recurrence using the Thyroid Cancer Recurrence After RAI Therapy dataset, which consists of 383 patient records and 13 key clinical attributes. The evaluation was conducted using accuracy, precision, recall, F1-score, and area under the curve (AUC) metrics. The results show that the XGBClassifier is the best-performing model with an accuracy of 97.4% and an AUC of 0.95, demonstrating superior performance in handling the minority class. This research is expected to contribute to the development of more effective machine learning–based clinical decision support systems for predicting thyroid cancer recurrence after therapy.
Penerapan Metode Multi Objective Optimization by Ratio Analysis (MOORA) Dalam Rekomendasi Pembelian Sepeda Motor Kurnialensya, Taufik Kurnialensya
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.720

Abstract

When making a decision to purchase a motorcycle, it is necessary to consider several criteria. These include price, environmental impact, fuel consumption, design and technology, performance and power, availability of spare parts, after-sales service, warranty on spare parts and distance to the nearest service centre. This research aims to help consumers make informed decisions when buying motorbikes by using the Multi-Objective Optimisation by Ratio Analysis (MOORA) method. The MOORA method was selected because it can handle many criteria efficiently and simply, and produce very objective decisions. Nine criteria were used with four alternatives: motor A, motor B, motor C and motor D. The MOORA calculation results ranked motor C as the highest with a value of 1.90, motor B as the second highest with a value of 1.77, motor A as the third highest with a value of 1.25 and motor D as the fourth highest with a value of 1.20. This research proves that the MOORA method can effectively and accurately provide recommendations for making decisions about purchasing motorbikes with many criteria.
Penerapan Metode Simple Additive Weighting (SAW) Dalam  Sistem Pendukung Keputusan Pemilihan  Karyawan Terbaik CV. Lestari Indojaya Sonhaji; Sri Hartati; Sri Lestari
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.736

Abstract

One of the important factors in quality human resources in a company to be able to run the business process properly in accordance with the vision and mission that has been set by the company. CV. Lestari Indojaya is one of the companies engaged in procurement and education which was founded in 2000. In order to encourage the creation of increasing performance productivity, CV. Lestari Indojaya carries out the selection of the best employees. Decision support systems can help decision makers to get recommendations for the best employees using the SAW (Simple Additive Weighting) method. In this study, the data used came from internal data and external data. There are several criteria needed to help decision makers choose the best employees, namely discipline, quality of work, cooperation and behavior. Based on all the criteria and alternatives in this study, Darwansyah was the best employee at CV. Lestari Indojaya with a total preference value of 2.875. The SAW method is an effective and practical method in calculating the best employee recommendations at CV. Lestari Indojaya so that decision makers can consider these recommendations according to the specified priorities.
Analisis Model Implementasi Tracking Pengiriman Barang Menggunakan Waterfall dan Rapid Application Development Achlison, Unang; Teguh Santoso, Joseph; Rozikin, Khoirur; Diapoldo, Fujiama
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.758

Abstract

System Development Life Cycle (SDLC) is a common methodology used to develop information systems. The purpose of this study is to determine system development between the Waterfall and Rapid Application Development (RAD) models The advantage of using the waterfall method is that the quality of the resulting system will be good because the implementation is carried out in stages. The disadvantage of using the waterfall method is that the system development process takes a long time. The advantage of using the RAD Model method is that the integration of sensors/other applications can be processed faster and more effectively. The disadvantage of using the RAD method is the limitations of payment gateways, and chat bots.
Rancang Bangun Alat Peningkatan Keamanan Pintu Belakang Kendaraan Angkutan Barang Buang Turasno; M. Feri Ramadhan; Moch. Aziz Kurniawan
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.764

Abstract

Vehicles in Indonesia are an important part of moving people or goods from one place to the destination, one of which is the transportation of boxed goods. However, the lack of security for boxed goods transport vehicles has always been the target of burglary of goods cargo in the area of Jalan Perniagaan Barat Roa Malaka Tambora, West Jakarta, perpetrators of goods theft from boxed goods cars that are traveling. Therefore, it is necessary to have a security system that can notify the driver when the back door of the vehicle is broken into, namely a Security Detection Tool on Iot-Based Goods Transport Vehicles Using Telegram this tool is able to detect the movement of the thief and evidence will be sent via Telegram. The research method used in this research is research adopting the ADDIE model R&D method, namely Analysis, Design, Development, Implementation, and Evaluation (assessment) of the reference model (ADDIE). In implementing the design of a Security Detection Tool on Iot-Based Goods Transport Vehicles Using Telegram Using the Esp32 Cam Microcontroller, MCU Node, and PIR sensors. Based on the Security Detection Tool, the transportation of box goods works well. Using the PIR Sensor, which is tested and applied directly to the vehicle. The tool is capable of sending telegrams in the form of messages, locations, and photos of the break-in.
Pemodelan Dinamika Baterai LiFePo4 Berbasis Model Rangkaian Equivalent Thevenin Orde-1 Muhamad Dzaky Ashidqi; Silviana Windaasari; Mardiyan Dama; Adi Affandi Ratib
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.697

Abstract

Accurate battery modeling is crucial for the development of battery-based energy storage systems, especially for real-time control and energy management applications. This study proposes a dynamic parameter modeling approach for LiFePO₄ batteries using a first-order Thevenin equivalent circuit model. Parameter estimation is performed to obtain the internal battery parameters based on the Thevenin model, and the parameter dynamics are derived using the Euler numerical method to represent battery behavior during charging and discharging processes. Model validation is conducted by comparing the predicted terminal voltage with actual measurements using root mean square error (RMSE) and mean absolute error (MAE) as evaluation metrics. The results show that the model accurately captures the battery dynamics, with an RMSE of 0.233 and an MAE of 0.047. Therefore, the proposed model is suitable for real-world applications that require accurate and dynamic estimation of internal battery parameters.
Improved Website SecurityUsing SSL and HAProxy Based PFSense Routers Ade Frihadi; Silviana Windasari; Mardiyan Dama; Bayu Bagaskoro; Abdurohman, Abdurohman
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.888

Abstract

Website security is a crucial aspect in the face of increasing cyber threats. This study aims to implement website protection using SSL (Secure Sockets Layer) and HAProxy (High Availability Proxy) through a PFSense router, and to evaluate its effectiveness using the securityheaders.com platform. The methodology used is an experimental study with a reverse proxy server configuration based on HAProxy and SSL certificates from Let's Encrypt. Measurement results indicate an improvement in the website's security score after the configuration was applied, particularly in security headers such as Strict-Transport-Security, X-Frame-Options, and Content-Security-Policy. This research contributes to strengthening web security architecture that is simple, efficient, and openly adoptable by government institutions and medium-scale organizations that require open-source-based security solutions.

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