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Jurnal Teknoinfo
Published by Universitas Teknokrat Indonesia
ISSN : 16930010     EISSN : 2615224X     DOI : https://doi.org/10.33365/teknoinfo
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science
Jurnal Teknoinfo is a peer-reviewed scientific Open Access journal that published by Universitas Teknokrat Indonesia. This Journal is built with the aim to expand and create innovation concepts, theories, paradigms, perspectives and methodologies in the sciences of Informatics Engineering. The articles published in this journal can be the result of conceptual thinking, ideas, innovation, creativity, best practices, book review and research results that have been done. Jurnal Teknoinfo publishes scientific articles twice a year in January and July.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 31 Documents
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SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN BIBIT DURIAN UNGGUL MENGGUNAKAN METODE AHP PADA CANDRA DUREN Candra Pangestu; Irma Rofni Wulandari; Sharazita Dyah Anggita; Ninik Tri Hartanti
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v19i2.519

Abstract

Salah satu faktor penentu dalam keberhasilan budidaya durian adalah pemilihan bibit durian. Toko Candra Duren merupakan sebuah toko penjualan bibit dan buah durian yang berlokasi di Desa Alasmalang Kabupaten Banyumas. Candra Duren menentukan bibit durian unggul dengan cara mengamati satu demi satu tekstur dari bibit durian tersebut. Desa Alasmalang telah dikenal sebagai daerah yang subur dan cocok untuk pertumbuhan tanaman durian, sehingga banyak petani yang memanfaatkan lahan untuk budidaya bibit buah durian termasuk toko Candra Duren. Pada toko Candra Duren terdapat varietas bibit durian unggulan yang di budidayakan antara lain Bawor, Musangking, Duri hitam, dan Chani. Cara pemilihan bibit durian unggul yang digunakan toko candra duren memiliki kelemahan yaitu seringkali penjual mengalami kesalahan dalam menentukan bibit durian unggul, proses dilakukan secara manual dan tidak adanya data yang tersimpan mengenai bibit durian dalam bentuk tulisan kertas maupun digital. Hal tersebut menyebabkan resiko kesalahan yang besar dalam penentuan bibit durian unggul. Sistem pengambilan keputusan banyak digunakan untuk membantu menyelesaikan suatu permasalahan dengan memilih suatu alternatif yang terbaik. Terdapat banyak metode yang dapat digunakan untuk analisis pendukung keputusan yang dapat menyelesaikan permasalahan pengambilan keputusan salah satunya adalah metode Analytical Hierarchy Process (AHP). Penelitian ini dilakukan untuk merancang sistem pendukung keputusan pemilihan bibit durian menggunakan metode AHP. Perancangan sistem dimulai dari tahap identifikasi masalah, pengumpulan data, analisis perancangan sistem, implementasi sistem dan pengujian sistem yang nantinya akan di implementasaikan ke program sistem pendukung keputusan pemilihan bibit durian berbasis website. Implementasi sistem ini menggunakan framework laravel versi 10 dengan bahasa pemograman PHP dan MySql sebagai basis datanya. Kriteria yang digunakan dalam penelitian ini adalah batang, daun, percabangan dahan, tinggi, dan umur. Bedasarkan penelitian yang di lakukan, AHP berhasil diterapkan pada sistem pendukung keputusan pemilihan bibit durian unggul sehingga dapat menampilkan perankingan. Hasil pengujian blackbox testing menunjukkan valid sesuai hasil yang diharapkan.
PERANCANGAN SISTEM PENJUALAN BERBASIS WEBSITE MENGGUNAKAN FRAMEWORK LARAVEL PADA PT. PARAMA SPEKTA INFININDO Aloisius Laurens Ananda Putra; Yemima Monica Geasela
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v19i2.558

Abstract

Pesatnya perkembangan teknologi informasi di era digital saat ini telah mendorong berbagai sektor usaha untuk melakukan transformasi digital, termasuk dalam sistem penjualan. Teknologi informasi memiliki peran penting dalam mendukung efisiensi operasional dan meningkatkan kualitas pelayanan terhadap pelanggan. Dengan memanfaatkan teknologi, perusahaan dapat menghemat waktu, memproses data dengan lebih akurat, serta memberikan pengalaman layanan yang lebih baik dan cepat kepada konsumen. Namun, kenyataannya masih ada perusahaan yang menjalankan proses penjualan secara manual. Salah satunya adalah pencatatan data pelanggan yang masih menggunakan Excel, perhitungan harga yang dilakukan dengan kalkulator, serta promosi produk yang belum maksimal dan belum memanfaatkan media digital secara optimal. Proses manual seperti ini dapat menyebabkan terjadinya kesalahan pencatatan, keterlambatan pelayanan, serta menyulitkan dalam melakukan pemantauan data penjualan secara real-time. Penelitian ini bertujuan untuk merancang sistem penjualan berbasis website yang dapat menjadi solusi dari berbagai kendala tersebut. Dengan adanya sistem ini, pelanggan dapat melakukan pemesanan produk secara online kapan saja dan di mana saja, mengunggah file desain mereka, serta melihat estimasi harga secara otomatis berdasarkan input layanan dan ukuran. Selain itu, data transaksi dan pelanggan akan tercatat secara sistematis di dalam database. Metode yang digunakan dalam penelitian ini adalah metode Waterfall, yang terdiri dari tahap analisis kebutuhan, desain sistem, pengkodean, dan pengujian. Dalam proses pengembangannya, digunakan framework Laravel karena mendukung arsitektur Model-View-Controller (MVC), yang membantu memisahkan logika aplikasi dan tampilan sehingga pengembangan sistem menjadi lebih terstruktur dan mudah dipelihara. Hasil dari penelitian ini menunjukkan bahwa sistem yang dirancang dapat berfungsi sesuai kebutuhan dan mampu meningkatkan efisiensi proses penjualan perusahaan secara keseluruhan.
PERANCANGAN HUMAN RESOURCE INFORMATION SYSTEM BERBASIS WEB PADA CV. CIPTA SARANA MAKMUR Abednego Marcello; Bhustomy Hakim
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The process of collecting and managing human resource data—such as attendance records, employee information, job assignments, and payroll—at CV. Cipta Sarana Makmur is currently carried out manually. This manual approach has resulted in several significant operational issues, including a high risk of data damage or loss, administrative delays, and increased workloads due to the lack of an integrated system. Furthermore, the conventional method often leads to data duplication and input errors, which negatively affect the accuracy of information, time efficiency, and the reliability of managerial decision-making. To address the identified issues, this study focuses on designing and developing a web-based Human Resource Information System (HRIS) capable of automating and integrating all human resource data management processes in an efficient and effective manner. The system development process adopts the Software Development Life Cycle (SDLC) methodology using the Waterfall model, which consists of five key phases: requirements analysis, system design, implementation, testing, and maintenance. Each phase is carried out in a structured and sequential manner to ensure the accuracy and completeness of the system being developed. Laravel was selected as the development framework due to its modular structure based on the Model-View-Controller (MVC) architecture, comprehensive documentation, and flexibility that supports efficient programming and long-term system scalability. The results demonstrate that the developed HRIS successfully meets the company’s operational needs by centralising and integrating various human resource functions. The system enables digital and structured processes for attendance tracking, employee data management, job assignment handling, and payroll processing. Its implementation significantly reduces data processing errors, avoids information redundancy, and accelerates administrative workflows that previously required considerable time when performed manually. Additionally, the system enhances data transparency and facilitates real-time information tracking, which supports better managerial oversight and decision-making. In conclusion, the HRIS developed in this study effectively addresses the core issues faced by CV. Cipta Sarana Makmur in managing human resource operations. To maximise the system’s potential, it is recommended that comprehensive training be provided to system users. For future system enhancements, the integration of more advanced technologies—such as facial recognition-based attendance systems—is advised to further improve accuracy, efficiency, and overall data security. Keyword: laravel, human resource information system, SDLC waterfall, data management, HRIS.
SISTEM PERINGATAN DINI KANTUK PENGEMUDI MENGGUNAKAN MODEL YOLOV11N BERBASIS CITRA WAJAH Adi Supriyatna; Deny Kurniawan; Mochamad Wahyudi; Lise Pujiastuti; Sumanto Sumanto; Dedi Triyanto
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v19i2.732

Abstract

Kecelakaan lalu lintas akibat kantuk saat mengemudi merupakan salah satu penyebab utama kematian di jalan raya dan menjadi isu keselamatan yang krusial. Studi menunjukkan bahwa 20–30% kecelakaan disebabkan oleh pengemudi yang mengantuk, sehingga diperlukan sistem peringatan dini yang mampu mendeteksi kondisi ini secara akurat dan real-time. Penelitian ini bertujuan untuk mengembangkan model deteksi kantuk berbasis visi komputer menggunakan algoritma YOLOv11n, yang dikenal sebagai varian ringan dan cepat dari keluarga YOLO. Model dilatih menggunakan dataset citra wajah yang telah diproses dan diaugmentasi melalui platform Roboflow, dengan tujuan untuk mendeteksi tanda-tanda kantuk secara visual. Hasil evaluasi model menunjukkan performa yang sangat baik, dengan nilai mAP50 sebesar 0,9710 dan mAP50-95 sebesar 0,6796. Selain itu, precision mencapai 0,9382 dan recall sebesar 0,9280, yang mengindikasikan kemampuan deteksi yang tinggi serta tingkat kesalahan yang rendah. Temuan ini membuktikan bahwa YOLOv11n dapat diimplementasikan secara efektif dalam sistem peringatan dini untuk meningkatkan keselamatan pengemudi, bahkan pada perangkat dengan sumber daya terbatas. Penelitian ini tidak hanya menjawab tantangan efisiensi dan akurasi deteksi kantuk, tetapi juga memberikan kontribusi nyata bagi pengembangan sistem keselamatan kendaraan berbasis kecerdasan buatan. Ke depan, pengembangan sistem deteksi multimodal yang menggabungkan citra wajah dengan data fisiologis seperti EOG dan detak kepala disarankan untuk meningkatkan keandalan sistem dalam kondisi nyata.
KOMPARASI ALGORITMA K-NEAREST NEIGHBOR, SUPPORT VECTOR MACHINE, DAN NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DAUN JERUK Deny Kurniawan; Dedi Triyanto; Mochamad Wahyudi; Lise Pujiastuti; Sumanto Sumanto; indra Chaidir
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v19i2.751

Abstract

Jeruk merupakan salah satu buah tropis yang banyak dikonsumsi masyarakat karena kandungan nutrisinya yang tinggi, khususnya vitamin C. Namun, produksi jeruk kerap mengalami penurunan akibat serangan penyakit, terutama pada bagian daun. Identifikasi penyakit secara manual dinilai kurang efisien dan rawan kesalahan, sehingga diperlukan sistem otomatis berbasis machine learning untuk membantu proses deteksi secara cepat dan akurat. Penelitian ini bertujuan untuk membandingkan tiga algoritma klasifikasi K-Nearest Neighbor (KNN), Support Vector Machine (SVM), dan Neural Network (NN) dalam mengidentifikasi penyakit daun jeruk berdasarkan fitur tekstur. Dataset yang digunakan terdiri dari lima kategori: Black Spot, Canker, Greening, Melanose, dan Healthy, dengan total 609 citra daun yang dibagi secara proporsional untuk pelatihan dan pengujian. Hasil evaluasi menunjukkan bahwa model Neural Network memberikan performa terbaik dengan akurasi 87,5%, diikuti oleh SVM sebesar 82,4%, dan KNN sebesar 77,5%. Penelitian ini menunjukkan bahwa pendekatan machine learning, khususnya Neural Network, efektif dalam klasifikasi penyakit daun jeruk dan berpotensi untuk diimplementasikan lebih lanjut dalam bentuk aplikasi praktis bagi petani.
Classification of Asphalt Road Damage Based on Images Using the Convolutional Neural Network (CNN) Method M. Rivan Padila; Arie Qurania; Mulyati Mulyati
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i1.771

Abstract

Damage to roads can cause inconvenience in driving and can even lead to accidents. Some of the damages that are often found on the road network are such as fine cracks, alligator skin cracks, potholes, asphalt grain release and others. The damage needs preventive handling because it is the main infrastructure in land transportation that is used every day plus areas with very high rainfall such as Indonesia, Damage to the road surface can occur more quickly. One method in artificial intelligence that can be used in identifying damaged roads is Convolutional Neural Networks (CNN). This method is capable of self-learning for object recognition, object extraction and classification and can be applied to high image resolution. The Citra data is taken from the results of google street view mapping with the application of the CNN model using YOLOv5, which is expected to be able to classify images specifically more effectively, objectively and safely in road maintenance efforts later. This research aims to classify image-based asphalt road damage using the Convolution Neural Network (CNN) method. The stages of this research consist of Data Selection, Preprocessing, Data Transformation, Data Mining and Pattern Evaluation using confusion matrix. The results obtained F1 score model of 73.5%, the value of mean Average Precision (mAP) of 75%, this shows that this model is able to classify fairly against all categories of data used.
Analysis of E-Commerce Applications Using the System Usability Scale (SUS) Approach Tri Wahyudi; Gunawan Budi Sulistyo; Nani Purwati; Noor Hasan
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i1.820

Abstract

This study aims to measure the level of satisfaction and effectiveness of the application, as well as the obstacles encountered, using the System Usability Scale (SUS) approach. The research employs a quantitative method with a questionnaire based on the System Usability Scale (SUS) to assess application usability. The population consists of Generation Z, with a sample of 411 respondents selected using cluster disproportionate random sampling. Data were analyzed to calculate the SUS score (0–100) and interpreted using the adjective scale. This study compares the usability of three e-commerce applications: Shopee, Tokopedia, and Lazada. The results indicate that the usability scores of the three e-commerce applications are relatively close: Shopee (90.06), Tokopedia (91.98), and Lazada (88.42). Based on gender, females prefer Shopee, while males favor Tokopedia and Lazada. In terms of age, the 20–24 age group is more dominant than the 15–19 age group. Meanwhile, based on occupation, students tend to use Lazada, whereas private employees prefer Shopee and Tokopedia. Shopee and Tokopedia demonstrate optimal performance, while Lazada has development potential, particularly for private employees and users aged 15–19. Further research is recommended to deepen the analysis of Lazada and include other regions and age groups for broader results.
Integrating Information Systems and Mathematical Models for UI/UX Design in Web-Based Digital Archives Adina Apriyani; Abdullah Ardi; Mega Wahyu Rhamadani
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i1.852

Abstract

The rapid growth of digital transformation in public institutions has underscored the urgency of developing effective and user-friendly digital archiving systems. In Indonesia, many government agencies, including the Regional Financial and Asset Management Agency (BKAD) of Kapuas Regency, still face inefficiencies in manual document management, with retrieval times averaging 15–20 minutes per file and high risks of data loss. This study aims to design and evaluate a web-based digital archive system that integrates information systems engineering with mathematical usability assessment, thereby addressing both functional and experiential challenges. The research employed the Design Thinking framework, progressing through empathize, define, ideate, prototype, and testing stages. Prototypes were developed using high-fidelity design tools, while usability evaluations combined subjective and objective measures through the System Usability Scale (SUS), Mission Usability Score (MIUS), and Maze Usability Score (MAUS). The findings demonstrate that the proposed system reduced retrieval times by 90 percent (from 20 minutes to 2 minutes) and achieved an SUS score of 82.5 (Excellent), a MIUS of 76.2 (Good), and a MAUS of 78.6 (Good), all surpassing benchmarks reported in previous studies. These results confirm that combining user-centered design with quantitative evaluation yields reliable outcomes. The study concludes that the hybrid evaluation framework provides both theoretical and practical contributions, while recommending further research on advanced features such as AI-based classification and large.
Development of a Plant Weed Detection Model Using the Mask R-CNN Algorithm for Smart Farming Budi Prayitno; Pritasari Palupiningsih; Atam Rifai Sujiwanto
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i1.873

Abstract

A more efficient and sustainable agricultural system is urgently needed during world population growth and global climate change. One of the main challenges is that ineffective weed management can significantly reduce crop yields. Conventional farming methods, such as large-scale herbicide application, also negatively impact the environment. Therefore, the development of smart farming technology based on artificial intelligence (AI) is a crucial innovative solution. This research is urgent in the context of developing AI-based systems that significantly contribute to agricultural technology. The urgency of this study is the creation of a plant weed detection model using deep learning to determine the readiness of planting land with high accuracy values. The importance of this research lies not only in the development of technology, but also in its contribution to the farmer economy and the progress of the agricultural sector in Indonesia. This research aims to build and develop a plant weed detection model using deep learning to determine the readiness of planting land, as well as evaluate the detection model built to produce high accuracy. The research method used follows a flow consisting of problem understanding, data understanding, data preprocessing, modelling, and evaluation. The deep learning method used is object detection by applying the Mask R-CNN algorithm with the ResNet-50 architecture as the backbone. The evaluation of model performance was carried out using Mean Average Precision (MAP). The results of this study demonstrated the development of a deep learning-based weed detection model using the Mask R-CNN algorithm, which achieved a MAP of 37.32 and was able to overcome the challenges of varying weed types, lighting conditions, and complex field conditions.
Comparison of SMOTE and ADASYN in Optimizing Random Forest Model for Imbalanced Financial Ratio Bankruptcy Prediction Novanda Rizky Ramadhana; Fuad Muhajirin Farid; Yeni Rahkmawati
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i1.1056

Abstract

Classification is a data analysis process that can predict classes based on predefined characteristics. In the era of big data, classification can be performed using machine learning. The problem of machine learning in classification analysis is imbalance data which often affect model performance. SMOTE and ADASYN are oversampling techniques to solve this problem. This study aims to evaluate the effectiveness of SMOTE and ADASYN in improving the performance of the Random Forest model on imbalanced data in the case of company bankruptcy using financial ratios. Models were built using training data with various splitting data and oversampling techniques. Then, the resulting models will be tested using testing data. The results show that the best model was achieved with a combination of splitting data 70:30 using SMOTE technique, which produced the highest f1-score of 40.57%, compared to ADASYN technique with 36.11% (a decrease of 4.46%), and without oversampling techniques with 19.51% (a decrease of 21.06%). The findings indicate SMOTE and ADASYN can identify minority values which are the main problem of imbalance data, with SMOTE showing better performance compared to ADASYN.

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