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All Journal Jurnal Algoritma Jurnal Wawasan Ilmiah Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) Jurnal Ilmu Sosial dan Ilmu Politik JOIN (Jurnal Online Informatika) Edu Komputika Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Industri JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Elektronika dan Telekomunikasi Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMATICS Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURTEKSI Jurnal Sistem Cerdas Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal PkM MIFTEK Jurnal Algoritma Engineering Science Letter IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Integrated : Journal Of Information Technology and Vocational Education JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of Intelligent Systems Technology and Informatics International Journal of Applied Information Systems and Informatics
Dede Kurniadi
Institut Teknologi Garut
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Social Sciences Other

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Benchmarking YOLOv8 Variants with Transfer Learning for Real-Time Detection and Classification of Road Cracks and Potholes Kurniadi, Dede; Latif, A. Abdul; Mulyani, Asri; Aulawi, Hilmi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6710

Abstract

Road damage, including potholes and cracks, is a significant issue frequently encountered in road infrastructure in many regions. Such conditions accelerate road degradation, increase the risk of traffic accidents, and significantly increase the maintenance and repair costs. Although several deep learning models have been proposed for road damage detection, few studies have systematically compared the performance of lightweight YOLOv8 variants using a consistent dataset. To address this gap, this study proposes a road defect detection and classification model based on the YOLOv8 series, which is enhanced using transfer learning to improve performance and efficiency. The dataset, obtained from Roboflow, comprises 3,846 images categorized into training, validation, and testing sets. Three YOLOv8 variants—YOLOv8n, YOLOv8s, and YOLOv8m—were benchmarked for performance. A performance evaluation was performed using the metrics of precision, recall, and mean Average Precision (mAP). Results show that YOLOv8m achieved the highest precision (99.00%), recall (98.40%), and mAP (99.50%). In the pothole category, precision reached 98.70% and recall 99.30%; in the crack category, precision was 99.30% and recall 97.60%. The findings demonstrate that YOLOv8, particularly the YOLOv8m variant, is highly effective for real-time road damage detection and classification, offering a viable solution for intelligent transportation systems and automated infrastructure monitoring. This research has the potential to revolutionize infrastructure monitoring by enabling scalable, real-time, and cost-effective assessments of road conditions. It minimizes reliance on manual inspections, reduces human errors, and contributes to the development of intelligent transportation systems and predictive maintenance strategies.
A Comparison Analysis Between ResNET50 and XCeption for Handwritten Hangeul Character using Transfer Learning Kurniadi, Dede; Nurhaliza, Nabila Putri; Balilo Jr, Benedicto B.; Aulawi, Hilmi; Mulyani, Asri
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1606

Abstract

The enthusiasm for Korean pop culture in Indonesia has contributed to a growing interest in learning the Korean language, including its writing system, Hangeul, which currently ranks as the 6th most studied language. Hangeul has a unique structure, where each character is arranged in syllabic blocks of consonants and vowel combinations. The main challenge in Korean character classification lies in the similarity between characters and the complex structure, making it more difficult for models to recognize. This study aims to compare two deep convolutional neural networks are ResNet50 and Xception, using transfer learning for handwritten Hangeul character classification. While previous studies have examined CNN-based character recognition, this study highlights the effectiveness of deeper architectures with limited yet augmented data. Unlike earlier works, it incorporates Grad-CAM visualizations, transfer learning with partial fine-tuning, and multiple train-test ratios to analyze model behavior. A total of 1,920 images across 24 classes were evaluated using 5-fold cross-validation, with extensive augmentation and preprocessing to simulate variation. The Machine Learning Life Cycle (MLLC) framework assessed model performance through accuracy, precision, recall, F1-score, and AUC. Both models achieved high performance, with ResNet50 consistently outperforming Xception in most folds, especially in precision and F1-score. ResNet50 achieved perfect scores (100%) across all metrics, while Xception also performed strongly with up to 99.74% accuracy. These results indicate that ResNet50 is more effective in classifying Korean letters on the dataset used in this study. For future research, a robustness evaluation can be applied using data that was not included in previous training or testing.
Optimization of Malaria Cell Image Classification Using Pretrained Resnet50 Architecture with Data Augmentation and Fine-Tuning Mulyani, Asri; Kurniadi, Dede; Rahmat, Agil
Engineering Science Letter Vol. 4 No. 02 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001244

Abstract

Malaria remains a significant health concern, particularly in tropical regions such as Indonesia, where timely and accurate diagnosis is crucial for reducing transmission and mortality. Conventional diagnosis through microscopic examination is labor-intensive, time-consuming, and highly dependent on expert availability. This study proposes an automated malaria cell image classification model using a deep learning approach based on the pretrained ResNet50 architecture. The research framework adopts the SEMMA (Sample, Explore, Modify, Model, Assess) methodology to structure the development workflow. A total of 27,558 labeled blood cell images comprising two balanced classes, Parasitized and Uninfected, were used for training and evaluation. Two model configurations were tested: a baseline model without data augmentation or fine-tuning, and an optimized model that integrates both. Augmentation techniques such as rotation, flipping, shearing, zoom, and brightness adjustment were applied to increase data diversity, while fine-tuning involved unfreezing the last 20 layers of ResNet50 to adapt pretrained features to the malaria domain. Performance was evaluated using accuracy, precision, recall, F1-score, loss, and AUC-ROC. The optimized model achieved 97.63% accuracy, 0.996 AUC-ROC, and 0.2472 loss, outperforming the baseline accuracy of 92.84%. An ablation study analyzed the individual contributions of augmentation and fine-tuning, showing that both techniques play complementary roles, with fine-tuning having the greater impact. A McNemar test confirmed that the improvements were statistically significant (p < 0.05). These findings demonstrate that the optimized ResNet50 model is effective for malaria detection and holds promise for integration into real-time diagnostic systems in resource-constrained environments.
Improving Low-Light Face Recognition using DeepFace Embedding and Multi-Layer Perceptron Kurniadi, Dede; Fernando, Erick; Fauziyah, Asyifa; Mulyani, Asri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6797

Abstract

Facial recognition systems often struggle under extreme lighting conditions, which distort facial features and reduce recognition accuracy. This study introduces a novel integration of DeepFace embeddings with a lightweight Multi-Layer Perceptron (MLP) classifier tailored to improve facial recognition under extreme lighting conditions. This combination has not been explored in previous studies and offers a compact alternative to conventional CNN-based methods. The Labeled Faces in the Wild (LFW) dataset was augmented using rotation, flipping, and lighting variations, and further enhanced with CLAHE for improved contrast under poor illumination. The resulting 128-dimensional DeepFace embeddings were classified using a four-layer MLP with LeakyReLU activation, Batch Normalization, and Dropout. The model was evaluated across three data-splitting schemes (70:30, 80:20, and 90:10), with the 80:20 configuration achieving the highest accuracy of 95.16%. Compared to the baseline CNN, the proposed method demonstrated superior robustness to illumination variations. This makes the proposed model suitable for real-time applications such as biometric authentication and AI-based surveillance systems.
Perancangan Model Convolutional Neural Network Pada Aplikasi Pengenalan Aksara Sunda Berbasis Mobile Kurniadi, Dede; Zulkarnaen, Ade Iskandar; Mulyani, Asri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 5: Oktober 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Mendikbudristek pada tahun 2022 menjadikan 38 bahasa daerah menjadi objek revitalisasi, salah satunya yaitu bahasa Sunda. Hal tersebut dikarenakan sebagian besar  bahasa daerah di Indonesia kondisinya terancam punah dan kritis. Hilangnya bahasa daerah juga mengancam keberadaan aksara lokal yang menjadi bagian integral dari warisan budaya. Dalam upaya memperkenalkan serta mendukung revitalisasi di bidang kebahasaan, pengembangan aplikasi media pembelajaran berbasis mobile dapat menjadi solusi yang efektif. Hal ini didasarkan bahwa perangkat smartphone memiliki pengguna yang luas di kalangan masyarakat. Penelitian ini bertujuan untuk merancang dan mengimplementasikan model Convolutional Neural Network (CNN) untuk pengenalan aksara Sunda pada perangkat berbasis mobile. Model CNN diterapkan pada fitur belajar menulis untuk mengenali tulisan tangan aksara Sunda dan memberikan feedback kepada pengguna. Penelitian ini menggunakan metode Machine Learning Lifecycle (MLLC) dimana tahapan yang dilakukan meliputi problem definition, data, model, dan production system. Penelitian dimulai dengan pembuatan dataset tulisan tangan digital, yang kemudian digunakan untuk melatih model klasifikasi menggunakan arsitektur CNN VGG-16. Dataset yang berhasil dibuat sebanyak 7500 gambar yang terdiri dari aksara Sunda swara, ngalagena, dan ngalagena serapan. Model yang dihasilkan dari proses pelatihan dengan total 10 epoch memperoleh akurasi sebesar 99%, sementara pada data testing memperoleh akurasi rata-rata sebesar 83%. Pada tahap akhir pengujian, model diimplementasikan pada prototype aplikasi pengenalan aksara Sunda berbasis mobile dan dapat dengan baik mengklasifikasi aksara Sunda. Hasil dari penelitian ini yaitu berupa model pengenalan aksara Sunda yang dapat diterapkan pada aplikasi berbasis mobile. Melalui pembuatan model dan prototype aplikasi pengenalan aksara Sunda, penelitian ini ikut berkontribusi pada digitalisasi aksara Sunda serta menyediakan landasan untuk pengembangan dan penelitian lanjutan dalam penerapan CNN pada aplikasi berbasis mobile.   Abstract In 2022, the Minister of Education, Culture, Research and Technology made 38 regional languages ​​the object of revitalization, one of which is Sundanese. This is because most regional languages ​​in Indonesia are endangered and critical. The loss of regional languages ​​also threatens the existence of local scripts, which are an integral part of cultural heritage. To introduce and support revitalization in the language field, the development of mobile-based learning media applications can be an effective solution. This is based on the fact that smartphone devices have a wide user base in society. This study aims to design and implement a Convolutional Neural Network (CNN) model for Sundanese script recognition on mobile-based devices. The CNN model is applied to the learning-to-write feature to recognize Sundanese handwriting and provide user feedback. This study uses the Machine Learning Lifecycle (MLLC) method, where the stages include problem definition, data, models, and production systems. The study began with creating a digital handwriting dataset, which was then used to train a classification model using the CNN VGG-16 architecture. The successfully created dataset was 7500 images consisting of Sundanese swara, ngalagena, and ngalagena absorption scripts. The model produced from the training process with 10 epochs obtained an accuracy of 99%, while the testing data obtained an average accuracy of 83%. In the final stage of testing, the model was implemented on a mobile-based Sundanese script recognition application prototype and could classify Sundanese script well. The results of this study are in the form of a Sundanese script recognition model that is applied to a mobile-based application prototype. By creating a model and prototype of a Sundanese script recognition application, this research contributes to the digitalization of Sundanese script and provides a foundation for further development and research in the application of CNN to mobile base applications.
Perancangan Aplikasi Text To Speech Dalam Bahasa Indonesia Menggunakan Firebase Machine Learning Kit Berbasis Android Kurniadi, Dede; Nuraeni, Fitri; Raharja, Indra Trisna; Mulyani, Asri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 6: Desember 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022965985

Abstract

Aplikasi text to speech dapat merubah teks menjadi keluaran suara menggunakan engine text to speech, namun teks tersebut harus berupa teks digital agar bisa di render. Sehingga, jika teks berada pada suatu objek maka harus diekstrak terlebih dahulu. Firebase Machine Learning Kit menyediakan API text recognition untuk membantu proses ekstrak teks. Firebase Machine Learning Kit (ML-Kit) juga menyediakan API language identifier untuk mendeteksi bahasa pada teks yang dibaca sehingga suara yang dikeluarkan dari teks yang dibaca dapat optimal dengan menggunakan dialek bahasa tertentu. Tujuan dari penelitian ini adalah membangun aplikasi text to speech dalam Bahasa Indonesia dengan penerapan Firebase Machine Learning Kit berbasis android. Dalam membangun aplikasi ini menggunakan metode extreme programming yang tahapannya terdiri dari planning, design, coding, dan testing. Hasil dari penelitian ini, berupa aplikasi yang dapat digunakan sebagai alat bantu pembelajaran bahasa asing dan alat digitaisasi teks serta terjemah ke dalam Bahasa Indonesia dan 34 dialek bahasa untuk keluaran suara text to speech. Selain itu, pada penelitian ini didapatkan nilai akurasi pengenalan teks dari tulisan tangan dan tulisan mesin, dengan rata-rata persentase akurasi untuk tulisan tangan sebesar 85,25%, sedangkan rata-rata persentase akurasi untuk tulisan mesin sebesar 87,35%. Dengan akurasi yang baik tersebut, maka aplikasi siap untuk dipergunakan sebagai alat bantu dalam proses pembelajaran bahasa asing oleh masyarakat Indonesia. AbstractText to speech applications can convert text into voice output using a text to speech engine, but the text must be digital text in order to render. So, if the text is in an object, it must be extracted first. The Firebase Machine Learning Kit provides a text recognition API to help extract text. The Firebase Machine Learning Kit (ML-Kit) also provides a language identifier API to detect the language in the text being read so that the sound emitted from the text read can be optimized by using a specific language dialect. The purpose of this research is to build a text to speech application in Indonesian with the application of an Android-based Firebase Machine Learning Kit. In building this application using the extreme programming method whose stages consist of planning, design, coding, and testing. The results of this study are in the form of applications that can be used as foreign language learning aids and text digitization tools and translations into Indonesian and 34 language dialects for text to speech voice output. In addition, in this study, the accuracy of text recognition from handwriting and machine writing was obtained, with an average percentage of accuracy for handwriting of 85.25%, while the average percentage of accuracy for machine writing was 87,35%. With good accuracy, the application is ready to be used as a tool in the process of learning foreign languages by the Indonesian people.
Sistem Informasi Geografis Pemetaan Data Terpadu Kesejahteraan Sosial di Kabupaten Garut Kurniadi, Dede; Mulyani, Asri; Firmansyah, Marshal; Abania, Nia
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 6: Desember 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022956098

Abstract

Dinas Sosial Kabupaten Garut berusaha dalam meningkatkan pelayanannya kepada masyarakat terutama dalam transparansi jumlah Data Terpadu Kesejahteraan Sosial (DTKS) per Kecamatan di Kabupaten Garut. Tetapi Dinas Sosial Kabupaten Garut saat ini belum mempunyai sistem informasi geografis yang menyediakan informasi jumlah DTKS per Kecamatan di Kabupaten Garut kepada masyarakat. Tujuan dari penelitian ini membangun sistem informasi geografis pemetaan data terpadu kesejahteraan sosial untuk memudahkan masyarakat mengetahui informasi jumlah DTKS per Kecamatan di Kabupaten Garut dengan memanfaatkan Teknologi Sistem Informasi Geografis (SIG). Metode yang digunakan adalah Rapid Application Development (RAD), dengan menggunakan tiga tahapan yaitu requirements planning, RAD design workshop, dan implementation. Bahasa pemrograman yang digunakan PHP dengan DBMS MySQL, dan Leaflet JavaScript Library. Penelitian ini menghasilkan Sistem Informasi Geografis Pemetaan Data Terpadu Kesejahteraan Sosial di Kabupaten Garut yang memiliki fitur peta DTKS kecamatan, pencarian data, fitur login super admin dan admin, serta fitur pengelolaan semua data oleh admin dan super admin. Penggunaan Metode RAD telah berhasil mengefektifkan waktu dalam pembangunan SIG ini, disamping hal tersebut hasil penilaian blackbox testing menunjukan hasil pengujian telah memenuhi semua hasil yang diharapkan oleh pengguna pada kebutuhan fungsional, dengan hasil tersebut diharapkan dapat memudahkan masyarakat untuk mengetahui informasi dan memeriksa status terdaftar di DTKS salah satunya melalui fitur pencarian data berdasarkan NIK (Nomor Induk Kewarganegaraan). AbstractThe Garut Regency Dinas Sosial is trying to improve its services to the community, especially in the transparency of the amount of Social Welfare Integrated Data (DTKS) per District in the Garut Regency. However, the Garut Regency Dinas Sosial currently does not have a geographic information system that provides information on the number of DTKS per sub-district in the Garut Regency to the public. This study aims to build a geographic information system for integrated social welfare data mapping to make it easier for the public to find information on the number of DTKS per sub-district in Garut Regency by utilizing Geographic Information System (GIS) technology. The method used is Rapid Application Development (RAD), using three stages, namely requirements planning, RAD design workshop, and implementation. The programming language used is PHP with MySQL DBMS and Leaflet JavaScript Library. This research resulted in a Geographical Mapping Information System for Social Welfare Integrated Data in Garut Regency, which features a sub-district DTKS map, data search, super admin and admin login features, and features for managing all data by admin and super admin. The RAD method has succeeded in streamlining time in the construction of this GIS. In addition to this, the results of the BlackBox testing assessment show that the test results have met all the results expected by users on functional requirements, with these results expected to make it easier for the public to find information and check the registered status in DTKS, one of which is through the data search feature based on NIK (Citizenship Identification Number).
Klasifikasi Masyarakat Penerima Bantuan Langsung Tunai Dana Desa Menggunakan Naïve Bayes dan SMOTE Kurniadi, Dede; Nuraeni, Fitri; Firmansyah, Marshal
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 2: April 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20236453

Abstract

Pemerintah menyelenggarakan program Bantuan Langsung Tunai Dana Desa (BLT DD), program ini memberikan (subsidi) kepada keluarga miskin yang memenuhi syarat. Program ini dapat membantu mengurangi beban pengeluaran serta meningkatkan pendapatan keluarga miskin. Masyarakat yang berhak menerima BLT DD terkadang melebihi kuota yang tersedia, kemudian proses penentuan penerima dilakukan secara musyawarah. Hasil penetapan tersebut terkadang menimbulkan kecemburuan sosial di masyarakat, sehingga diperlukan klasifikasi yang dapat membantu menentukan keluarga yang layak menerima program bantuan ini. Penelitian ini bertujuan untuk menerapkan metode Naïve Bayes untuk mengklasifikasikan data keluarga layak dan tidak layak menerima BLT DD karena masih banyak keluarga miskin berpenghasilan rendah lainnya yang belum berkesempatan untuk memperoleh program bantuan ini. Metode penelitian yang digunakan yaitu Cross-Industry Standard Process For Data Mining (CRISP-DM). Data yang digunakan merupakan data penerima BLT DD tahun 2021 dan 2022 di Desa Kersamenak dengan jumlah data yang digunakan sebanyak 375, meliputi class layak 205 record dan tidak layak 170 record. Data yang terkumpul menunjukkan adanya ketidakseimbangan kelas pada jumlah masyarakat yang layak dan tidak layak, sehingga diperlukan teknik Synthetic Minority Over-sampling Technique (SMOTE) untuk menangani kelas yang tidak seimbang pada data. Hasil pemodelan Naïve Bayes menggunakan teknik SMOTE menghasilkan model performansi terbaik dengan nilai akurasi 97,80% dan nilai AUC 0,99 yang termasuk dalam kategori Excellent Classification. Berdasarkan hasil model kinerja klasifikasi yang diperoleh, model yang dihasilkan dapat diimplementasikan ke dalam sistem aplikasi pendukung keputusan untuk membantu Desa dalam menentukan penerima BLT DD agar lebih cepat dan mudah. Abstract The government organizes the Bantuan Langsung Tunai Dana Desa (BLT DD) program, which provides (subsidies) to low-income families who meet the requirements. This program can help reduce the burden of spending and increase the income of low-income families. Communities who deserve to receive BLT DD sometimes exceed the available quota, then the process of determining the recipient is carried out utilizing deliberation. The results of these determinations sometimes cause social jealousy in the community, so a classification is needed that can help determine eligible families to receive this assistance program. This study aims to apply the Naïve Bayes method to classify family data as eligible and not eligible to receive BLT DD because there are still many other low-income families who have not had the opportunity to acquire this assistance program. The research method used is Cross-Industry Standard Process for Data Mining (CRISP-DM). The data used is the data of the 2021 and 2022 Village Fund Direct Cash Aid recipients in Kersamenak Village, with the amount of data used as much as 375, including 205 eligible class records and 170 inappropriate records. The data collected shows an imbalanced class in the number of eligible and ineligible people, and it is necessary to use the Synthetic Minority Over-sampling Technique (SMOTE) technique to handle the imbalanced class in the data. The results of modeling the Naïve Bayes using SMOTE technique produce the best performance model with an accuracy value of 97.80% and an AUC value of 0.99, which is included in the Excellent Classification category. Based on the results of the classification performance model obtained, we can implement the resulting model into a decision support application system to assist the Village in determining the recipient of the BLT DD to make it faster and easier.
Analisis Penerimaan Learning Management System Institut Teknologi Garut Menggunakan Technology Acceptance Model Mulyani, Asri; Kurniadi, Dede; Putri, Mita Hidayani
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106618

Abstract

Kemajuan teknologi dari masa ke masa terus berkembang secara pesat dalam bermacam bidang salah satunya dalam bidang pendidikan. Pendidikan mempunyai kedudukan yang sangat berarti dalam upaya kenaikan mutu seseorang, tetapi dengan kemunculan wabah penyakit Corona Virus Disease 2019 (Covid-19) menyebabkan lahirnya tatanan gaya hidup baru secara global. Civitas akademika Institut Teknologi Garut selain mematuhi peraturan dari pemerintah juga mengikuti perkembangan pendidikan berbasis teknologi informasi yang bersifat interaktif dengan menggunakan aplikasi Learning Management System sebagai pendukung proses pembelajaran jarak jauh dimasa wabah penyakit Covid-19. Dikarenakan Learning Management System (LMS)  di Institut Teknologi Garut baru digunakan, maka penelitian ini bertujuan menganalisis penerimaan Learning Management System Institut Teknologi Garut menggunakan metode Technology Acceptance Model, untuk mengetahui pengukuran pengaruh antar konstruk sekaligus sebagai barometer adaptasi penerimaan pengguna terhadap sistem LMS yang digunakan. Dalam penelitian ini pengolahan data analisis memakai Structural Equation Modeling melalui tools Statistical Product and Service Solution dan Analysis of Moment Structures. Penelitian ini menghasilkan tingkat penerimaan pengguna terhadap Learning Management System dengan nilai probabilitas dibawah 5% yaitu 0,000 dan pengaruh antar konstruk Technology Acceptance Model dengan 3 hipotesis yang diterima ialah variabel Persepsi kemudahan memengaruhi Persepsi kegunaan, Persepsi kegunaan memengaruhi Niat penggunaan, dan Niat penggunaan memengaruhi Penggunaan nyata. AbstractTechnological advances from time to time continue to develop rapidly in various fields, one of which is in the field of education. Education has a very significant role in efforts to improve one's quality, but the emergence of the Corona Virus Disease 2019 outbreak has led to the birth of a new lifestyle order globally. In addition to complying with government regulations, the Garut Institute of Technology academic community also follows the development of interactive information technology-based education with the use of informative applications through electronic media in order to get efficient results, namely the Learning Management System. Because the Learning Management System at the Garut Institute of Technology has just been used, a study entitled Learning Management System Acceptance Analysis of the Garut Institute of Technology uses the Technology Acceptance Model Method, to determine the measurement of the influence between constructs as well as a benchmark for adapting user acceptance to the system used. In this research, data analysis is processed using Structural Equation Modeling through Statistical Product and Service Solution tools and Analysis of Moment Structures. This study resulted in the level of user acceptance of the Learning Management System with a probability value below 5%, namely 0.000 and the influence between the constructs of the Technology Acceptance Model with 3 accepted hypotheses, namely the variable Perception of convenience affects Perception of usefulness, Perception of usefulness affects Intention of use, and Intention of use affects Real use . It is hoped that the results of this research can be used as a reference for developers to continue to optimize the functionality of the Learning Management System so that it can be used optimally.
Sistem Rekomendasi Pemilihan Pengepul Limbah di PT. Pituku Cordova International Menggunakan Algoritma Haversine Kurniadi, Dede; Sutedi, Ade; Nursyaban, Dzikri; Mulyani, Asri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241117694

Abstract

Seiring dengan semakin banyaknya mitra serta volume pesanan pada platformPituku menjadikan pihak perusahaan kesulitan dalam menentukan pengepul limbah yang cocok untuk menangani suatu pesanan. Idealnya, pengepul yang dipilih merupakan pengepul yang terletak paling dekat secara geografis dengan pemesan limbah sehingga biaya pengiriman dapat di minimalkan dan pemesan dapat segera menerima limbah pesananannya. Oleh karena itu dibutuhkan suatu sistem rekomendasi yang dapat merekomendasikan daftar pengepul limbah yang diurutkan dari yang paling dekat ke pembeli limbah. Penelitian ini bertujuan untuk membuat sistem rekomendasi pemilihan pengepul limbah di PT. Pituku Cordova International yang dapat membantu merekomendasikan daftar pengepul limbah yang diurutkan dari yang paling dekat dengan pembeli limbah sehingga proses pemilihan pengepul limbah menjadi lebih efektif. Penelitian ini menggunakan metode Rapid Throwaway Prototyping Modelyang dimana tahapan yang dilakukan meliputi outline requirements, develop protoype, evaluate prototype, specify system, develop software, dan validate system. Algoritma Haversine formuladigunakan dalam sistem rekomendasi dimana koordinat garis lintang dan garis bujur dihitung untuk mendapatkan jarak antara pembeli dan pengepul limbah dalam satuan km kemudian berdasarkan jarak tersebut daftar pengepul limbah diurutkan dari yang paling dekat ke yang paling jauh. Metrik evaluasi menggunakan NDCG (Normalized Discounted Cumulative Gain) yangmengukur akurasi rangkingsistem rekomendasi. Berdasarkan hasil evaluasidiperoleh informasi bahwasistem rekomendasi memiliki score NDCG rata-rata sebesar 1 yang artinya sistem rekomendasi memberikan item rekomendasi dengan rangkingyang diharapkan
Co-Authors Abania, Nia Abdulah, Farhan Naufal Abdurrahman, Fauzan Abdussalam, Iqbal Abdussalam Abdusy Syakur Amin Ade Sutedi Ade Sutedi Ade Sutedi, Ade Adiwangsa, Alfian Akmal Agus Hermawan Agus Nugraha Agustiansyah, Yoga Ahmad Habib Lutfi Aisyah Fitri Islami Ajif, Arvin Muhammad Ajiz, Rafi Nurkholiq Akbar, Gugun Geusan Alamsyah, Renaldy Aldy Rialdy Atmadja Ali Djamhuri Alisha Fauzia, Fathia Alkamal, Chaerulsyah Alvin Zainal Musthafa Alwan Nul Hakim Amrulloh, Muhammad Fawaz Andri Saepuloh Aneu Suci Nurjanah Asri Indah Pertiwi Asri Mulyani Asri Rahayu Ningsih Ayu Suryani B. Balilo Jr , Benedicto B. Balilo Jr, Benedicto Balilo Jr, Benedicto B. Barlinti Maryam Budik Burhanuddin, Ridwan Cahya Mutiara Dede Sopiah Della Adelia Anugrah Detila Rostilawati Dewi Tresnawati Dhea Arynie Noor Annisa Diar Nur Rizky Diaz Radhian Salam Diazki, Moch Haiqal Diki Jaelani Dini Destiani Siti Fatimah Diva Nuratnika Rahayu Dudy Mohammad Arifin Dyka Afan Afthori Dzikri Nursyaban Efi Sofiah Elsen, Rickard Eri Satria Erick Fernando B311087192 Erwan Yani Erwan Yani, Erwan Erwin Gunadhi Rahayu, Raden Erwin Widianto Fadillah, Hadi Bagus Faisal, Ridwan Nur Fajar Rahman Faturrohman, Nadhif Fauziah, Fathia Alisha Fauziyah, Asyifa Fikri Zakaria Rahman Firmansyah, Marshal Fitri Nuraeni Fitriani, Ranti Fitriyani Gelar Panca Ginanjar Ghilman Hasbi Basith Gisna Fauzian Dermawan H. Bunyamin Hadi Wijaya, Tryana Haekal, Mohamad Fikri Hamzah Nurrifqi Fakhri Fikrillah Hari Ilham Nur Akbar Hasfi Syahrul Ramadhan Hazar, Aura Fitria Helmalia P, Nabilla Febriani Hendri Aji Pangestu Heri Johari Heri Suhendar Heri Suhendar Hilmi Aulawi Ida Farida Ikbal Lukmanul Hakim Ikhrom, Taufik Darul Ikmal Muhammad Fadhil Ilham Muhamad Ramdan Imas Dewi Ariyanti Inda Muliana Indra Trisna Raharja Indri Tri Julianto Indri Tri Julianto Intan Sri Fatmalasari Irawan, Muhammad Randy Irfan Qusaeri Irfanov, Muhammad Irsyad Ahmad Iskandar, Joko Jajang Jaenudin Jajang Romansyah Jembar, Tegar Hanafi Khaerunisa, Nisrina Khoerunisa, Sarah Kusmayadi, Kusmayadi Latif, A. Abdul Latifah, Ayu Leni Fitriani Leni Fitriani, Leni Lia Amelia Lindayani, Lindayani M. Mesa Fauzi Mahendra Akbar Musadad Maulana , Muhammad Arief Maulana, Ahmad Rakha Maulana, Ilham Ahmad Maulana, Yusep Maulina, Wina Senja Meta Regita Mochamad Deni Ramdani Muhamad Solihin Muhammad Abdul Yusup Hanifah Muhammad Affan Al Sidqi Muhammad Rikza Nashrulloh Muhammad Saleh Muhammad Sanusi Muhammad Wildan Muliana, Inda Muttaqin, Moch Riefky Chaerul Nita Nurliawati Nugraha, M Aldi Nugraha, Nikolas Pranata Nurfadillah, Rifa Sri Nurhaliza, Nabila Putri Nurlisina, Elisa Nurpatmah, Lisna Nursa'diah, Rifania Sapta Nursyaban, Dzikri Nurul Fauziah Nurul Khumaida Nurzaman, Muhammad Zein Omar Komarudin Pratama, Reifalga Gais Prayoga, Moch. Gumelar Putri, Mita Hidayani Raharja, Indra Trisna Rahayu, Diva Nuratnika Rahayu, Raden Erwin Gunadhi Rahmat, Agil Rahmi, Murni Lestari Rajab, Ilham Syahidatul Ramdhan, Dekha Ramdhani Hidayat Randy Wardan Ridwan Setiawan Ridwan Setiawan Ridwan Setiawan Ridwan Setiawan Rifky Muhammad Shidiq Rinda Cahyana Rinda Cahyana Risfiyanisa Fasha Rizki Fauziah Roeri Fajri Firdaus Rohman, Fauza Rohmanto, Ricky Rostina Sundayana Rubi Setiawan Rudi Sutrio Safei P, M Iqbal Ismail Sarah Khoerunisa Sermana, Elsa Maharani Sheny Puspita Indriyani Siti Rima Fauziyah Sofwan Hamdan Fikri Sopiah, Dede Sri Intan Multajam Sri Mulyani Lestari Sri Rahayu SRI RAHAYU Sri Rahayu Syahrul Sidiq Syaiffani, Moch Assami Tina Maryana Undang Indrajaya W, Faksi Ahmad Wahidah, Tania Agusviani Wiwit Septiani Yanti Sofiyanti Yayat Supriatna Yoga Handoko Agustin Yosep Septiana Yosep Septiana Yuni Yuliani Yusfar Ilhaqul Choer Yusuf Mauluddin Zaqiah, Neng Nufus Zulkarnaen, Ade Iskandar