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Rahmad Hidayat
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Jurnal Teknologi Rekayasa Informasi dan Komputer
Published by Politeknik Negeri Lhokseumawe
ISSN : 25812882     EISSN : 27971724     DOI : http://dx.doi.org/10.30811/jtrik.v8i1
Core Subject : Science,
Computer Science & IT
Jurnal Teknologi Rekayasa Informasi dan Komputer (JTRIK) merupakan media publikasi hasil penelitian yang diterbitkan oleh Politeknik Negeri Lhokseumawe. JTRIK dipublikasikan setiap 2 bulan yaitu maret dan september baik secara print dan online. Scope jurnal ini meliputi bidang ilmu komputer, pemrosesan citra, jaringan komputer, keamanan komputer, multimedia, pengembangan perangkat lunak dan internet of things
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 134 Documents
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Sistem Pakar Diagnosa Tingkat Depresi pada Mahasiswa Menggunakan Metode Fuzzy Tsukamoto Berbasis Web Yuliana, Yuliana; Arhami, Muhammad; Hendrawaty, Hendrawaty
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 6, No 1 (2023): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v6i1.4705

Abstract

Depression is a mental disorder in the nature of feelings or moods characterized by symptoms of moodiness,lethargy, no passion for life, feeling useless, deep disappointment, hopelessness, thoughts of death and suicidal ideation. Ifyou experience feelings of sadness and hopelessness, it is normal for someone to feel, but if the condition is experienced for months for no apparent reason. So it can be concluded that one of the signs of depression that appears. In general, depression can be divided into three levels, namely mild depression, moderate depression and severe depression. Depression can happen to everyone, including in the world of education such as students. The level of depression in college students has increased compared to the age of children and adults. People with depression tend not to pay attention to their diet and physical activity. In general, students do not know how much depression is experienced and students also do not have knowledge about how to prevent depression, so we need a system that can diagnose depression levels. Based on these problems, an expert system was designed to diagnose the level of depression in students using the Web-based Fuzzy Tsukamoto method. This system is designed for Lhokseumawe State Polytechnic students. This system uses the Fuzzy Tsukamoto method which is used to diagnose depression levels in students. The results of the percentage of test dataobtained in this system are 76%.Keywords — Expert System, Diagnosis, Depression, Fuzzy Tsukamoto, Web
Student Attendance System Using Fingerprint and Raspberry Pi with Notification Rasya, Muhammad; Atthariq, Atthariq; Nasir, Muhammad; Taufik, Taufik
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 8, No 1 (2025): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v8i1.7425

Abstract

Fingerprint is an application designed to meet the needs of fast data using fingerprint verification. A fingerprint attendance machine is a type of biometric attendance machine that uses the student attendance/attendance method by detecting fingerprints. This fingerprint attendance is designed with a Fingerprint Sensor, LCD, and notification components. The purpose of this final project is to design an attendance device with fingerprints and a web database as a means of attendance. This system uses a Raspberry Pi as the controller. The study used fingerprints that had previously been verified in the database. In testing this attendance system, there are four tests, namely LCD testing, which is based on the results of the LCD only displaying all forms of data as a display that is by the registered database, fingerprint testing, and 10 times fingerprint testing of 20 people can produce a read. 97.7% of this value can be attributed to the average success rate of the fingerprint. web testing based on the results that have been tested on the Selenium IDE tool with successful output and no errors, the web is available 2 seconds after the fingerprint The data will be stored directly in the web database. Application testing, based on the results of application testing, namely, after fingerprint data enters the web database and immediately notifies the parents' Gmail account of the information from the application that the student has taken attendance, and the parents can see the attendance data or informationfrom the student.
Sistem Pendeteksi Kualitas Daging Berbasis Android Mahgfira, Lana; Nasir, Muhammad; Jamilah, Jamilah
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 3, No 2 (2020): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v3i2.1888

Abstract

Abstrak— Daging merupakan bahan pangan yang penting dalam memenuhi kebutuhan gizi. Selain mutu proteinnya tinggi, pada daging terdapat pula kandungan asam amino esensial yang lengkap dan seimbang. Keunggulan lain, protein daging lebih mudah dicerna dibanding dengan yang berasal dari nabati. Pada umumnya penentuan kualitas daging di kalangan masyarakat masih menggunakan cara manual yang umumnya hanya melihat dengan kasat mata.karena mata manusia mempunyai keterbatasan sehingga hasilnya berbeda tergantung pada penglihatan mata. untuk mengatasi masalah tersebut dapat dibuat sebuah aplikasi sistem pendeteksi kualitas daging berbasis android. Penelitian ini bertujuan untuk menentukan kualitas daging menggunakan segmentasi dengan menggunakan metode thresholding dan HSV.Hasilnya menunjukan bahwa data penelitian yang diperoleh mampu menonjolkan area tertentu dari citra digital sehingga aplikasi dapat mendeteksi kualitas daging secara efisien dan praktis dengan waktu relatif lebih singkat dan dapat di gunakan masyarakat luas dengan keakuratan deteksi kualitas daging berkisar antara 70-80 %. Kata Kunci: Android, Eclipse,Color Segmentation.Thresholding,HSV.
Penerapan Iot (Internet of Things) Untuk Kontrol Kebakaran Dikota Lhokseumawe Hidayat, Rahmat; Husaini*, Husaini; Hidayat, Hari Toha
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 2, No 2 (2019): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v2i2.2592

Abstract

Seiring dengan berkembangnya zaman manusia sudah banyak menciptakan inovasi baru pada masalah yang dihadapi manusia. Namun ada beberapa hal yang masih menjadi perhatian dalam menciptakan inovasi baru. Contohnya seperti penanganan kebakaran yang terjadi sekarang masih kurang efektif. Hal ini dikarenakan butuh waktu yang lumanyan lama untuk petugas pemadam kebakaran mengetahui bahwa adanya kebakaran. Sehubungan dengan keadaan tersebut mengakibatkan penanganan yang telat dari petugas karena lambatnya informasi yang diterima ataupun hal lain seperti pemadam yang susah dihubungi hal ini mendorong developer hardware untuk menciptakan sebuah perangkat untuk pendeteksian adanya api dan asap dapat menentukan arah titik api. Sensor (api dan asap) yang telah dipasangi dan ditempatkan pada sebuah tempat yang dapat digunakan sebagai identitas untuk mendeteksi adanya api dan asap. Alat yang digunakan tidak dapat berpindah dari satu tempat kepada tempat yang lain karena prototype yang dibuat adalah menetap. Sensor api akan berdeteksi saat api menyala kemudian sensor akan mengirim data yang diterima dan dikirimkan kewemos. Kemudian wemos akan memproses data tersebut dan mengirimkan keadmin melalui aplikasi telegram. Sehingga admin segera cepat mengetahui titik kebakaran dengan segera cepat menangganinya. Dalam penelitian ini juga menggunakan metode QoS (Quality of Service) untuk mengukur parameter delay dan Jitter yang dihasilkan dalam satu jaringan yang digunakan. Hasil pengujian yang telah dilakukan sebanyak 50 kali percobaan dalam penelitian ini delay dihasilkan -0.1007 second. Dan untuk hasil jitter adalah 0.1819 dari hasil pengujian Jarak maksimum pembacaan sensor api dan sensor asap yaitu 1 cm. Dari hasil 50 kali percobaan, persentase keberhasilan sistem dalam mengirim notifikasi berupa titik kebakaran dan kegagalan sistem dalam mengirim notifikasi yaitu 20%.
Implementasi Private Cloud Storage dengan Nextcloud pada Jaringan Virtual Private Server Mujibbullah, Mujibbullah; Amri, Amri; Safar, Ilham
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 7, No 2 (2024): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v7i2.4403

Abstract

Dalam era globalisasi yang berkembang pesat, kemajuan teknologi telah mengubah cara kita menyimpan, berbagi data, dan berkomunikasi secara signifikan. Kendala pada media penyimpanan tradisional dan kebutuhan akan solusi penyimpanan yang efisien telah mendorong penggunaan teknologi Cloud Storage. Cloud Storage, yang memanfaatkan server virtual untuk menyimpan data digital, telah menjadi solusi yang semakin relevan dalam mengatasi keterbatasan media penyimpanan fisik seperti hardisk. Salah satu platform yang memanfaatkan teknologi ini adalah Nextcloud, yang berfungsi sebagai media penyimpanan cloud. Penelitian ini bertujuan untuk mengevaluasi sejauh mana Nextcloud dapat membantu dalam aspek-aspek tersebut dan mengukur performa jaringan dengan memanfaatkan parameter Quality of Service (QoS). Hasil pengujian Qos diperoleh rata-rata throughput 2410 kbps, packet loss 0,050%, delay 5,113ms, dan jitter sebesar 5,115ms, dengan demikian dapat disimpilkan bahwa menurut standar tiphon dapat dikategorikan “Bagus”.
Klasifikasi Citra Daging Ayam Dengan Menggunakan Metode K-Nearest Neighbor Husna, Asmaul; Indrawati, Indrawati; Nasir, Muhammad
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 1, No 1 (2018): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v1i1.1853

Abstract

Proses pengklasifikasian tingkat kesegaran daging ayam berdasarkan ciri warna dan tekstur. Sistem ini juga dapat digunakan untuk membedakan daging ayam segar maupun Yang kurang segar. Metode identifikasi tingkat kesegaran daging ayam yang digunakan pada penelitian ini adalah menggunakan pengolahan citra digital yaitu ekstrasi ciri warna metode HSV dan ekstrasi ciri tekstur metode GLCM. yang akan diklasifikasi menggunakan metode K-Nearest Neighbor (KNN).
Application of K-Means and Web-Based GIS For Poverty Mapping In The Aceh Region Irwansyah, Irwansyah; Mulyadi, Mulyadi; Huzaeni, Huzaeni
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 9, No 1 (2026): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v9i1.8707

Abstract

Poverty is a serious problem facing Indonesia, especially in developing regions such as Aceh, and can hinder national progress. Poverty involves various aspects, such as economic conditions, housing, and individual social capabilities. The variability of poverty data between regions is influenced by various factors, including social assistance, income, and other social factors. Therefore, solutions are needed to better manage and understand poverty data in Aceh. Two approaches that can be taken to overcome this challenge are the k-means clustering method and geographic information systems (GIS). The k-means clustering method is a statistical tool that allows researchers to group regions based on poverty levels, helping to identify relevant patterns. Meanwhile, GIS is used to analyse and visualise poverty data in the form of maps, facilitating understanding of poverty distribution patterns in Aceh. The development of a web-based geographic information system can also facilitate public and government access to poverty data in Aceh, increasing transparency and participation in addressing poverty issues. The results of this system will produce three groups of regions, namely non-poor, poor and very poor. Furthermore, this system has an accuracy of 93% similarity with the results conducted by BPS Lhokseumawe and Alu O Village.
Design And Development of A Monitoring And Prediction System For Sari Roti Returns In Aceh Utara Using The Single Exponential Smoothing Method Kamil, Farhan; Huzaeni, Huzaeni; Amirullah, Amirullah
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 9, No 1 (2026): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v9i1.8758

Abstract

This research focuses on inventory management of Sari Roti branded bread products in North Aceh, Indonesia, which faces financial loss problems due to unexpected product returns. Currently, return calculations are done manually, but this method is still inadequate. This study aims to develop a bread inventory prediction system using the single exponential smoothing method. In this context, Sari Roti is a leading bread manufacturer with various types of bread products. The main problem is the difficulty in estimating the quantity of bread that will be returned by stores in the region. To improve inventory estimation accuracy, this research introduces a prediction and monitoring system that enables stores to track the amount of bread stock to be supplied and the quantity of returns by salespeople each week. The method used is Single Exponential Smoothing, which seeks to improve upon the previous method that had a high error rate. This research is expected to help sales personnel and Sari Roti distributors in North Aceh predict bread return quantities more accurately, with the goal of reducing financial losses due to unexpected returns. The use of the Single Exponential Smoothing method to predict returns and drops for the 10 most common Sari Roti products from each store in North Aceh with an alpha value of 0.3 is quite good, because the prediction results obtained using Mean Absolute Deviation (MAD) error evaluation yielded an average error value below 10%, while the prediction results obtained using Mean Absolute Percentage Error (MAPE) evaluation are not yet satisfactory, because the average error value obtained is below 40%.
Application of the K-Nearest Neighbors (KNN) Algorithm for Diabetes Mellitus Classification: Evidence from Aceh Province, Indonesia Aulia Safira Rahman, Cut; Aulia, Rizky Wahyu; Azhar, Azhar; Hidayat, Rahmad
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 9, No 1 (2026): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v9i1.8721

Abstract

Diabetes mellitus is a non-communicable disease with a steadily increasing prevalence in Indonesia, including Aceh Province. Early detection using data-driven approaches is essential to minimize the risk of severe complications. This study aims to classify diabetes mellitus by implementing the K-Nearest Neighbors (KNN) algorithm. The dataset comprises 1,500 instances from the Pima Indians Diabetes Dataset obtained from Kaggle and an additional 100 instances collected from hospitals across Aceh Province. Data preprocessing involved normalization and label encoding, followed by data partitioning into training and testing sets using a 90:10 ratio. The KNN model was configured with a parameter value of K=5. Experimental results indicate that the proposed model achieved an accuracy of 85%, precision of 87%, recall of 82%, and an F1-score of 85% on the Kaggle dataset. For the hospital dataset, the model attained an accuracy of 76%, precision of 80.95%, recall of 68%, and an F1-score of 73.91%. These findings suggest that the KNN algorithm demonstrates adequate performance in classifying diabetes mellitus and may serve as a basis for the development of data-driven medical decision support systems.
Efficient Paprika Leaf Disease Classification Using MobileNetV2-Based Deep Learning and Image Augmentation Herawati, Siti; Harist, Abdul; Angginta, Teguh Randi
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 9, No 1 (2026): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v9i1.8763

Abstract

The decline in productivity of paprika plants (Capsicum annuum) is often closely associated with leaf health disorders, particularly bacterial spot disease, which is difficult to identify at an early stage through conventional visual inspection. Early detection of this disease is crucial, as delayed diagnosis can lead to significant yield losses and reduced crop quality. However, manual monitoring relies heavily on expert knowledge and is prone to subjectivity and human error, especially in large-scale cultivation systems. To address this challenge, this study proposes a deep learning–based image classification approach for identifying diseases in paprika leaves by leveraging transfer learning with the MobileNetV2 architecture. MobileNetV2 was selected due to its lightweight structure and computational efficiency, making it suitable for practical and real-time agricultural applications. The dataset used in this research was obtained from the PlantVillage database and consists of two classes: healthy paprika leaves and leaves infected with bacterial spot disease.  To enhance the robustness and generalization capability of the proposed model, the training data were enriched using various data augmentation techniques, including rotation, flipping, scaling, and brightness adjustment. These techniques help mitigate overfitting and improve the model’s ability to recognize disease patterns under diverse imaging conditions. Experimental results demonstrate that the proposed model achieves stable and reliable classification performance, with an overall accuracy of 96%, accompanied by balanced precision and sensitivity values across both classes. These results indicate that MobileNetV2 is highly effective for paprika leaf disease classification. Furthermore, the findings suggest strong potential for implementing the proposed approach as an image-based plant disease detection system, supporting precision agriculture and enabling early intervention to improve crop productivity and sustainability.

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