Abstract:Abstract: Academic achievement mapping is an important process in higher education to support effective academic monitoring and guidance. In practice, student grouping is often conducted manually by academic staff using…
simple criteria such as Grade Point Average (GPA) thresholds and subjective judgment, without systematic data analysis. This study aims to apply the Fuzzy C-Means (FCM) clustering algorithm to objectively group students based on their academic achievement levels. The dataset consists of academic records from 179 sixth-semester students of the Computer Science Study Program at Universitas Islam Negeri Sumatera Utara, where 160 eligible students are processed in the FCM calculation. Three variables are used: cumulative GPA, total completed credits, and the total number of low grades (D/E). The FCM algorithm automatically performs the mapping and groups students into three categories, namely excellent, stable, and at-risk students. Cluster quality is evaluated using the Silhouette Score and Davies–Bouldin Index, showing satisfactory clustering performance. The results indicate that the proposed approach provides a data-driven and objective basis for academic decision support.
Keywords: academic achievement; clustering; fuzzy c-means; student
Abstrak: Pemetaan pencapaian akademik mahasiswa merupakan proses penting dalam pendidikan tinggi untuk mendukung pemantauan dan pembinaan akademik yang tepat sasaran. Dalam praktiknya, pengelompokan mahasiswa masih sering dilakukan secara manual oleh pihak akademik berdasarkan kriteria sederhana, seperti batasan Indeks Prestasi Kumulatif (IPK) dan penilaian subjektif, tanpa analisis data yang sistematis. Penelitian ini bertujuan menerapkan algoritma Fuzzy C-Means (FCM) untuk mengelompokkan mahasiswa secara objektif berdasarkan tingkat pencapaian akademik. Data penelitian berasal dari 179 mahasiswa semester enam Program Studi Ilmu Komputer Universitas Islam Negeri Sumatera Utara, dengan 160 mahasiswa memenuhi kriteria dan diproses menggunakan algoritma FCM. Variabel yang digunakan meliputi IPK kumulatif, jumlah SKS yang telah ditempuh, dan total nilai rendah (D/E). Proses pemetaan sepenuhnya dilakukan oleh algoritma FCM dan menghasilkan tiga kategori mahasiswa, yaitu unggul, stabil, dan berisiko. Evaluasi menggunakan Silhouette Score dan Davies–Bouldin Index menunjukkan kualitas pengelompokan yang cukup baik.
Kata kunci: fuzzy c-means; clustering; mahasiswa; pencapaian akademik
Abstract:Abstract: Malaria remains a major global health problem, particularly in tropical countries such as Indonesia. Accurate early diagnosis is essential for reducing malaria-related morbidity and mortality. Conventional microscopic…
oscopic examination is time-consuming, highly dependent on expert personnel, and prone to human error. This study compares the performance of two Convolutional Neural Network (CNN) architectures, ResNet-50 and DenseNet-121, for malaria image classification. The Cell Images for Malaria dataset provided by the National Institutes of Health (NIH) through Kaggle was used, consisting of 27,558 microscopic blood cell images categorized into Parasitized and Uninfected classes. The dataset was divided into 80% training data and 20% testing data. Image preprocessing included resizing to 224 × 224 pixels, normalization, labeling, and data augmentation using RandomFlip, RandomRotation, RandomZoom, and RandomContrast. Experimental results showed that the ResNet-50 model trained for 100 epochs achieved the highest performance, with an accuracy of 95.54% and precision, recall, and F1-score of 0.96. The confusion matrix indicated 5,272 correctly classified images out of 5,510 testing samples. These findings demonstrate that ResNet-50 outperformed DenseNet-121 and has strong potential for supporting accurate, reliable, and efficient computer-aided malaria diagnosis based on microscopic blood smear images.
Keywords: computer-aided diagnosis; convolutional neural network (CNN); densenet-121; early detection; image classification; malaria; microscopic blood smear images; resnet-50;
Abstrak : Malaria masih menjadi masalah kesehatan global yang serius, terutama di negara tropis seperti Indonesia. Diagnosis dini yang akurat sangat penting untuk menurunkan angka morbiditas dan mortalitas. Metode konvensional berupa pemeriksaan mikroskopis memiliki keterbatasan karena memerlukan waktu yang relatif lama, bergantung pada tenaga ahli, dan berpotensi menimbulkan kesalahan manusia. Penelitian ini bertujuan membandingkan kinerja arsitektur Convolutional Neural Network (CNN) yaitu ResNet-50 dan DenseNet-121 dalam klasifikasi citra malaria. Dataset yang digunakan berasal dari Cell Images for Malaria yang disediakan oleh National Institutes of Health (NIH) melalui platform Kaggle, terdiri dari 27.558 citra dengan pembagian 80% data latih, 20% data validasi. Tahap praproses meliputi cleaning, resizing citra menjadi 224×224 piksel, normalisasi, labeling, serta data augmentasi menggunakan RandomFlip, RandomRotation, RandomZoom, dan RandomContrast. Hasil pengujian menunjukkan bahwa model ResNet-50 pada epoch 100 memperoleh akurasi sebesar 95,54% dengan nilai precision, recall, dan F1-score masing-masing sebesar 0,96. Confusion matrix menunjukkan jumlah prediksi benar sebanyak 5.272 dari total 5.510 data uji. Hasil ini menunjukkan bahwa arsitektur CNN mampu mengklasifikasikan citra malaria dengan tingkat akurasi yang tinggi dan memiliki kemampuan generalisasi yang baik terhadap data baru. Penelitian ini memberikan kontribusi dalam evaluasi performa arsitektur CNN untuk mendukung pengembangan sistem diagnosis malaria berbasis citra mikroskopis yang lebih cepat dan akurat.
Kata kunci: convolutional neural network (CNN); citra mikroskopis hapusan darah; densenet-121; diagnosis berbantuan komputer; deteksi dini; klasifikasi citra; malaria; resnet-50
Abstract:Abstract: The implementation of dress code regulations in university environments is generally still carried out conventionally, requiring significant time and effort and potentially leading to subjective assessments. This…
is study develops an automatic student dress code compliance detection system using computer vision based on the YOLOv8 model. The dataset consists of 1,800 annotated images divided into eight clothing categories, split into 78% training (1,404 images), 14% validation (254 images), and 8% testing (143 images). All images underwent preprocessing and data augmentation before training the YOLOv8 model with an input size of 640×640 pixels for 50 epochs. During testing, the YOLOv8 model achieved an overall performance of Precision 0.844, Recall 0.773, F1-Score 0.802, and mAP@0.5 0.841, and was able to detect clothing objects with good accuracy and stable performance under various image conditions. The system was integrated with a Flask-based backend and a web-based frontend to enable real time detection and compliance classification, with a response time of less than 2 seconds, supporting automatic and consistent identification of student dress code compliance as “Compliant” or “Violation.”
Keywords: compliance detection; computer vision; dress code regulations; real time detection; YOLOv8.
Abstrak: Penerapan aturan berpakaian di lingkungan kampus umumnya masih dilakukan secara konvensional sehingga membutuhkan waktu dan tenaga yang relatif besar serta berpotensi menimbulkan subjektivitas penilaian. Penelitian ini bertujuan mengembangkan sistem pendeteksi kepatuhan berpakaian mahasiswa secara otomatis berbasis visi komputer menggunakan model YOLOv8. Dataset yang digunakan terdiri dari 1.800 citra beranotasi yang terbagi ke dalam 8 kategori pakaian, dengan pembagian data sebesar 78% data latih (1.404 citra), 14% data validasi (254 citra) dan 8% data uji (143 citra). Seluruh citra diproses melalui tahapan pre-processing dan data augmentation, kemudian digunakan untuk melatih model YOLOv8 dengan ukuran input 640×640 piksel selama 50 epoch. Pada tahap pengujian, model mencapai performa keseluruhan dengan Precision 0.844, Recall 0.773, F1-Score 0.802, dan mAP@0.5 0.841, serta mampu mendeteksi objek pakaian dengan akurasi baik dan performa stabil pada berbagai kondisi citra. Sistem kemudian diintegrasikan dengan backend berbasis Flask dan frontend web untuk mendukung proses deteksi waktu nyata dan klasifikasi kepatuhan, dengan waktu respons sistem kurang dari 2 detik, sehingga mampu mengidentifikasi status kepatuhan berpakaian mahasiswa ke dalam kategori “Aman” dan “Melanggar Aturan” secara otomatis dan konsisten.
Kata kunci: aturan berpakaian; deteksi waktu nyata; pendeteksi kepatuhan; visi komputer; YOLOv8.
Abstract:Abstract: INET Computer Palembang, as a computer training institution, faces difficulties in understanding participant characteristics due to variations in age, educational background, and chosen course packages. This study…
udy aims to analyze participant criteria and group them based on similarities using the K-Means Clustering algorithm. The data used were historical records of course participants from 2022 to 2025. The research process followed the CRISP-DM stages, starting from data cleaning and transformation, determining the optimal number of clusters using the Elbow Method, to evaluating cluster quality with the Davies-Bouldin Index. The implementation was carried out using Python and the scikit-learn library. The results show that the optimal number of clusters is k=5 with a Sum of Squared Errors (SSE) value of 1064.66 and a Davies-Bouldin Index (DBI) score of 0.820, indicating good cluster quality. The resulting clustering provides a structured profile of participants and demonstrates that K-Means is effective in segmenting course participants. These findings are expected to assist the institution in designing more targeted training programs.
Keywords: clustering; data mining; elbow method; k-means; computer course
Abstract:Abstract: Computer networks are not just additional facilities in the campus environment, but computer networks help the overall academic activities and social relations of students. This research aims to overcome the problem…
oblem of uneven wifi internet networks and less than optimal SSID management in UHAMKA flats, which has an impact on student access to information and communication. The method used is PPDIOO with simulation using Cisco Packet Tracer and the chosen star topology to provide a stable connection and easy network management. The results of the simulation show that all devices are well connected to each other, as indicated by the successful IP ping test between devices. The research concluded that the PPDIOO method was successful in designing an effective and structured internet network in the students' living environment. So that it can improve access to academic activities and good communication.
Keywords: cisco packet tracer; computer networks; PPDIOO
Abstrak: Jaringan komputer bukan hanya sekedar fasilitas tambahan dalam lingkungan kampus, tetapi jaringan komputer membantu keseluruan aktivitas akademik dan hubungan sosial mahasiswa. Penelitian ini bertujuan mengatasi permasalahan jaringan internet wifi yang belum merata dan pengelolaan SSID yang kurang optimal di rusunawa UHAMKA, sehingga berdampak pada akses informasi dan komunikasi mahasiswa. Metode yang digunakan adalah PPDIOO dengan simulasi menggunakan cisco packet tracer dan topologi star yang dipilih untuk memberikan koneksi stabil dan pengelolaan jaringan yang mudah. Hasil dari simulasi menunjukan seluruh perangkat saling terhubung dengan baik, ditandai dengan berhasilnya pengujian ping IP antar perangkat. Penelitian menyimpulkan metode PPDIOO berhasil dalam merancang jaringan internet yang efektif dan terstruktur di lingkungan tempat tinggal mahasiswa. Sehingga dapat meningkatkan akses aktivitas akademik dan komunikasi secara baik.
Kata kunci: cisco packet tracer; jaringan komputer; PPDIOO
Abstract:Face recognition has become a common thing used in the field of surveillance and security in computer technology and image devices. This study aims to identify the usefulness of a person's face on 3 test images. This study…
dy examines the methods of cropping techniques, image enhancement through intensity measurement, and histogram analysis to improve the contrast and distribution of image intensity. In addition, the Viola-Jones algorithm is used to detect key facial features such as eyes, nose, and mouth. The results of the analysis are then applied in the feature evaluation stage, where usually between facial features are applied to measure the ratio of facial proportions. Furthermore, the comparison of proportional ratios of several images was analyzed using bar graphs and line graphs to evaluate the trend and stability of facial proportions. The results showed the best ratio stability with a smaller variation of the on-off ratio of image 2 which is 0.4762 pixels to 0.4983 pixels. Image 2 is the most ideal for face measurement systems based on geometric ratios because it provides more consistent and visible results.
Abstract:Abstract: Face detection remains a challenging task in computer vision due to real-world factors such as uneven lighting, varying viewpoints, distance, and occlusion. This study aims to develop and evaluate a real-time facial…
acial feature detection application (detecting face, eyes, nose, and mouth) using MATLAB and a webcam. Detection is performed using the Viola-Jones Cascade Classifier method through the vision.CascadeObjectDetector function. Key parameters that were adjusted include the MergeThreshold (ranging from 4 to 50 depending on the feature) and MinSize (based on estimated feature size within the frame). However, this study does not include tuning of other parameters such as FalseAlarmRate, which constitutes a limitation of the employed method. The adjustment of these parameters proved significant in improving detection accuracy and robustness under varying lighting conditions. Nevertheless, the system still encounters difficulties in detecting facial features in the presence of occlusion. This study also has the potential to serve as a foundation for further developments in face recognition, emotion detection, or biometric authentication.
Keywords: computer vision; haar cascade; MATLAB
Abstrak: Deteksi wajah merupakan tantangan dalam visi komputer karena dipengaruhi oleh kondisi nyata seperti pencahayaan tidak merata, sudut pandang, jarak, dan obstruksi. Penelitian ini bertujuan untuk mengembangkan dan menguji aplikasi deteksi fitur wajah secara real-time (wajah, mata, hidung, dan mulut) menggunakan MATLAB dan kamera webcam. Deteksi dilakukan dengan metode Viola-Jones Cascade Classifier melalui fungsi vision.CascadeObjectDetector. Parameter penting yang disesuaikan adalah MergeThreshold (antara 4 hingga 50 tergantung fitur), MinSize (mengikuti estimasi ukuran fitur dalam frame). Namun, penelitian ini tidak mencakup penyesuaian parameter lain seperti FalseAlarmRate, yang menjadi salah satu keterbatasan metode yang digunakan. Penyesuaian parameter ini terbukti signifikan dalam meningkatkan akurasi deteksi dan ketahanan terhadap variasi kondisi pencahayaan. Namun, sistem masih mengalami kesulitan mendeteksi fitur wajah jika terjadi obstruksi. Penelitian ini juga berpotensi menjadi dasar untuk pengembangan lebih lanjut dalam face recognition, emotion detection, atau biometric authentication.
Kata kunci: visi computer; haar cascade; MATLAB
Abstract:Abstract: Both hardware and software technologies offer their advantages in helping to facili- tate student learning activities. Virtual reality technology allows users to interact directly with the virtual reality environment,…
ironment, giving the effect of a pleasant learning sensation because it pro- vides direct experience for students to actively do desktop computer assembly practicum inde- pendently and guided. This research is R & D (Research and Development), which aims to pro- duce a product as a desktop computer assembly virtual reality learning application. This re- search procedure adapts the Lee & Owens development model. The subjects of this research were students at the Open University, Makassar State University, and Lambung Mangkurat University. The results showed that using Virtual Reality in desktop computer assembly can provide extraordinary experiences to users, bridging the gap between the real and virtual worlds. This is achieved through specially designed hardware to create a virtual environment that re- sembles the actual reality or even creates an entirely new reality.
Keywords: desktop computer; assembly; virtual reality
Abstrak: Teknologi perangkat keras (hardware) maupun lunak (software) menawarkan keunggulannya dalam membantu memfasilitasi aktivitas belajar dan pembelajaran ma- hasiswa. Teknologi virtual reality memiliki kemampuan bagi penggunanya untuk dapat melakukan interaksi langsung dengan lingkungan realitas maya, memberi efek sensasi pembelajaran yang menyenangkan karena memberikan pengalaman langsung bagi ma- hasiswa untuk aktif melakukan pratikum perakitan computer desktop secara mandiri maupun terbimbing. Penelitian ini adalah R & D (Research and Development) yang ber- tujuan untuk menghasilkan suatu produk yaitu berupa aplikasi pembelajaran virtual real- ity perakitan computer desktop. Prosedur penelitian ini mengadaptasi model pengem- bangan Lee & Owens. Subjek penelitian ini adalah mahasiswa pada Universitas Ter- buka, Universitas Negeri Makassar, dan Unibversitas Lambung Mangkurat. Hasil penelitian diperoleh bahwa penggunaan Virtual Reality dalam perakitan computer desk- top mampu memberikan pengalaman luar biasa kepada pengguna, menjembatani jurang antara dunia nyata dan dunia maya. Hal ini dicapai melalui penggunaan perangkat keras yang dirancang khusus untuk menciptakan lingkungan virtual yang menyerupai realitas sebenarnya atau bahkan menciptakan realitas yang sama sekali baru.
Kata kunci: computer desktop; perakitan; virtual reality
Abstract:Abstract: CV. Ria Kencana Ungu (RKU), as a research partner in the field of computer service, needs to improve the quality of customer service and efficiency in the process of troubleshooting computer damage. To meet these…
se needs, an expert system based on the forward chaining method was developed that is able to diagnose damage automatically. This system was developed using the waterfall method, with systematic stages from analysis to implementation. The implementation results show that the system can identify the type of damage with an accuracy rate of 89% based on validation tests on 100 real troubleshooting cases. The evaluation metric uses a comparison between the results of the system diagnosis and the results of the technician's analysis. Although the system is able to increase service efficiency by up to 40% compared to conventional methods, several obstacles were found, such as the limited initial knowledge base that impacts the accuracy of the diagnosis and the difficulty of users in understanding the system interface. Therefore, further development is needed to expand the knowledge base and improve the user experience. This study aims to develop a forward chaining-based expert system to improve efficiency, accuracy, and speed of problem solving at CV. Ria Kencana Ungu (RKU) and to increase customer satisfaction through more responsive and precise services..
Keywords: expert system; computer troubleshooting; forward chaining method
Abstrak: CV. Ria Kencana Ungu (RKU), sebagai mitra penelitian di bidang layanan servis komputer, membutuhkan peningkatan kualitas layanan pelanggan dan efisiensi dalam proses troubleshooting kerusakan komputer. Untuk memenuhi kebutuhan tersebut, dikembangkan sistem pakar berbasis metode forward chaining yang mampu mendiagnosis kerusakan secara otomatis. Sistem ini dikembangkan menggunakan metode waterfall, dengan tahapan yang sistematis dari analisis hingga implementasi. Hasil implementasi menunjukkan bahwa sistem dapat mengidentifikasi jenis kerusakan dengan tingkat akurasi sebesar 89% berdasarkan uji validasi terhadap 100 kasus troubleshooting nyata. Metrik evaluasi menggunakan perbandingan antara hasil diagnosis sistem dan hasil analisis teknisi. Meskipun sistem mampu meningkatkan efisiensi layanan hingga 40% dibandingkan metode konvensional, beberapa kendala ditemukan, seperti keterbatasan basis pengetahuan awal yang berdampak pada akurasi diagnosis dan kesulitan pengguna dalam memahami antarmuka sistem. Oleh karena itu, pengembangan lebih lanjut diperlukan untuk memperluas basis pengetahuan dan meningkatkan pengalaman pengguna. Penelitian ini bertujuan mengembangkan sistem pakar berbasis forward chaining untuk meningkatkan efisiensi, akurasi, dan kecepatan troubleshooting di CV. Ria Kencana Ungu (RKU) serta meningkatkan kepuasan pelanggan melalui layanan yang lebih responsif dan presisi.
Kata kunci: sistem pakar; troubleshooting komputer; metode forward chaining
Abstract:Abstract: Data on stunting cases among toddlers in Datuk Bandar Timur sub-district, Tanjung Balai city Not yet processed in a way computerized use digital map , so that the monitoring process in-depth investigation of the…
e distribution and patterns of stunting in a area Not yet presented with Good . This matter Of course impact on cadre Public health center Semula Jadi For monitor number nutrition bad And spread prevalence of stunting in Datuk Bandar Timur sub-district, Tanjung Balai city . For That need development A system internal data management form chart nor map that can be give description spread nutrition bad in the region Mayor. Planning And development system information This done with use method data collection and interview direct to the UPTD Community Health Center Semula Jadi . Objective it was built this webgis can assist in identifying areas that need nutritional intervention, mapping the prevalence of stunting and malnutrition, as well as monitoring nutrition programs and optimizing resource allocation. Results from GIS can plays an important role in supporting efforts to prevent and overcome stunting problems by providing tools effective software for collecting, analyzing and visualizing nutritional data spatially. The use of web GIS can also provide information on stunting locations from UPTD Semula Jadi Health Center data.
Keywords : geographic information system; location mapping system; stunting.
Abstrak: Data kasus stunting pada balita di kecamatan datuk bandar timur kota tanjung balai belum diproses secara terkomputerisasi menggunakan peta digital, sehingga proses pemantauan penyelidikan mendalam tentang sebaran dan pola stunting disuatu daerah belum tersajikan dengan baik. Hal ini tentu berdampak pada kader puskesmas Semula Jadi untuk memantau angka gizi buruk dan penyebaran prevalensi stunting di kecamatan datuk bandar timur kota tanjung balai. untuk itu perlu pembangunan sebuah sistem pengelolaan data dalam bentuk grafik maupun peta yang dapat memberikan gambaran penyebaran gizi buruk diwilayah Datuk Bandar. Perancangan dan pembangunan sistem informasi ini dilakukan dengan menggunakan metode pengambilan data dan wawancara langsung ke UPTD Puskesmas Semula Jadi. Tujuan dibangunnya webgis ini dapat membantu dalam identifikasi daerah yang membutuhkan intervensi gizi, pemetaan prevalensi stunting dan gizi buruk, serta memantau program-program gizi dan optimalisasi alokasi sumber daya. Hasil dari SIG dapat berperan penting dalam mendukung upaya pencegahan dan penanggulangan masalah stunting dengan menyediakan alat software yang efektif untuk pengumpulan, analisis, dan visualisasi data gizi secara spasial. Kegunaan webgis juga dapat memberikan informasi mengenai lokasi stunting dari data UPTD Puskesmas Semula Jadi.
Kata kunci: sistem informasi geografis; sistem pemetaan lokasi; stunting