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Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer
Published by Universitas Mulawarman
ISSN : 18584853     EISSN : 25974963     DOI : -
Core Subject : Science,
Computer Science & IT
Journal Informatics Mulawarman Is a means for researchers in the field of computer science to publish his research works. First published in 2007 with a two-yearly published period in February and September. Editorial Board Informatika Mulawarman consists of lecturers of computer science in the field of diverse concentration of expertise among others Software Engineering, Information Systems, Network and Computer Security, Image Processing, Multimedia, fuzzy logic, human interface and Artificial Intelligence.
Arjuna Subject : -
Articles 297 Documents
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Implemantasi Rest API untuk E-Herbarium Alameka, Faza; Riyayatsyah, Riyayatsyah; Septiarini, Anindita; Hamdani, Hamdani; Hairah, Ummul; Puspitasari, Novitanti; Az Zahrah, Rezha Nur
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 18, No 2 (2023): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v18i2.12631

Abstract

e-herbarium yang ada pada lab Herbarium Wanariset adalah sebuah media yang mendokumentasikan tanaman-tanaman yang dikeringkan dalam bentuk digital. Khususnya untuk tumbuhan berjenis Dipterocarpaceae. Untuk memungkinkan para ahli-ahli kehutanan ataupun bidang ilmu lainnya dari dalam negeri maupun mancanegara menggunakan. E-herbarium menyediakan data tanaman yang dikeringkan berbasis digital sebagai data ontentik kegiatan penelitian dibidang botani, ekologi, taksonomi tumbuhan dan etnobontani. Untuk tujuan dari peneltian ini kami mengembangankan API (pemprograman aplikasi antarmuka) untuk e-herbarium yang memungkinkan para pengguna mengakses, menggunakan dan memanfaatkan data yang disuaikan kebutuhan dari para pengguna akses dari API tersebut dan para pengguna juga dapat berinteraksi dengan GUI untuk mengembangkan data herbarium khususnya data tanaman berjenis Dipterocarpaceae sesuai dengan pengguna tersebut berada Di penelitian ini menjelaskan organisasi dari data-data yang ada di e-herbarium dan penerapan API untuk penelitian ini. Penelitian ini menggunakan arsitektur REST API dalam untuk pengembangan dari API yang dibangun. memungkinkan berbagai sistem untuk berkomunikasi dan mengirim / menerima data dengan cara yang sangat sederhana. Lebih lebih lagi fungus dari api ini dapat mengimport dan eksport model data herbarium dalam format JSON untuk mengakses API tersebut. Data yang terbentuk merupakan hasil dari pengaksesan API melalui URL menggunakan token. Token dapat diperoleh dengan cara Generate Api Key yang merupakan fitur dari sistem yang kemudian dapat digunakan untuk mengakses API yang tersedia. Method yang ada didalam API ini hanya menggunakan GET. Dimana method ini digunakan bersamaan dengan argument yang tersedia sehingga pengguna dapat meminta data yang dibutuhkan saja dari API. Argumen digunakan melalui parameter URL dan dapat memilah berdasarkan nama, spesies, family, ecology, status konservasi, nama lain, persebaran, deskripsi.
Game Edukasi Mobile Learning Dengan Artificial Intelligence Untuk Meningkatkan Pemahaman Pengenalan Hardware Pada Platform Android Akhyar, Ramaulvi Muhammad; Fadhillah, Shendy Raihan; Rizieq, Aji Muhammad
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 18, No 2 (2023): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v18i2.17712

Abstract

Dalam era Revolusi Industri 4.0, sistem pendidikan tetap fokus pada pengembangan kreativitas, berpikir kritis, kerjasama, komunikasi, serta karakter siswa. Kemampuan di bidang teknologi, media, informasi, pembelajaran, inovasi, dan karir sangat diutamakan. Teknologi, terutama game edukasi berbasis mobile learning dengan dukungan artificial intelligence (AI), memiliki peran penting dalam proses pembelajaran.  Game edukasi ini dirancang untuk secara interaktif memperkenalkan perangkat keras kepada siswa. Dalam pengembangan game ini, AI digunakan untuk mengklasifikasikan gambar perangkat keras seperti Processor, GPU, Motherboard, RAM, HDD, SSD SATA, dan SSD M.2. Meskipun berhasil dalam pengujian, terdapat kesalahan dalam klasifikasi karena beberapa faktor seperti kualitas gambar yang buruk, kebisingan dalam gambar, dan perbedaan ukuran foto.  Oleh karena itu, saran untuk pengembangan berikutnya adalah melakukan pre-processing pada gambar yang diambil untuk meningkatkan akurasi AI. Dengan demikian, game edukasi mobile learning berbasis AI diharapkan dapat meningkatkan minat dan motivasi belajar siswa, sambil membantu guru dalam menyediakan pengalaman pembelajaran yang lebih menyenangkan dan interaktif. Hal ini sejalan dengan pendekatan modern dalam pendidikan yang mendukung perkembangan keterampilan esensial yang diperlukan di era Industri 4.0.
Traffic Accident Prediction Using Machine Learning Based on PT Jasa Raharja Data Utomo, Muhammad Fikri; Fikry, Muhammad; Hamdhana, Defry; Abdullah, Dahlan; Nurdin, Nurdin
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 21, No 1 (2026): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v21i1.25392

Abstract

Traffic accidents represent a critical issue that significantly affects public safety and generates substantial social and economic impacts, particularly within the operational area of PT. Jasa Raharja Lhokseumawe Branch. The lack of predictive information regarding accident occurrences often results in reactive policy making. This study aims to develop a machine learning–based forecasting model for traffic accident rates using a combination of K-Means Clustering and Recurrent Neural Network (RNN). The dataset consists of historical traffic accident records from 2022 to 2024, which were preprocessed and aggregated on a weekly basis at the district level. K-Means Clustering was employed to group districts according to weekly accident patterns, resulting in two optimal clusters based on silhouette score evaluation. Subsequently, separate RNN models were developed for each cluster to forecast weekly accident occurrences. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that the RNN model achieved higher prediction accuracy for clusters with more stable accident patterns compared to clusters exhibiting higher fluctuation. Overall, the proposed combination of clustering and RNN demonstrates strong potential in producing accurate traffic accident forecasts
Detection of Anemia Based on Conjunctival Images Using a Convolutional Neural Network (CNN) Method Sari, Rika Yulia; Fitri, Zahratul; Afrillia, Yesy
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 21, No 1 (2026): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v21i1.28906

Abstract

A hemoglobin level below 12 g/dL is the primary indicator of anemia, a condition commonly found in adolescent girls. Laboratory blood tests, as a conventional detection method, are invasive, time-consuming, and costly. This study developed a non-invasive classification system for anemia and non-anemia based on conjunctival images using a Convolutional Neural Network (CNN), implemented on a real-time website. A total of 433 conjunctival images were collected comprising 206 images of anemia and 227 of non-anemia sourced from smartphone cameras and the Kaggle dataset, divided in an 80:10:10 ratio for training, validation, and testing. Preprocessing included resizing to 150 150 pixels, augmentation (flip, rotation, zoom, translation, brightness), and pixel normalization. The CNN architecture consists of three convolutional layers (32, 64, and 128 filters), max pooling, dropout, and a fully connected layer with sigmoid activation, trained using the Adam optimizer and the binary cross-entropy loss function until the 43rd epoch. The model achieved an accuracy of 88.37%, precision of 0.89, recall of 0.88, and an F1-score of 0.88. The model was integrated with a Flask-based REST API and MediaPipe Face Landmarker to automatically detect the conjunctival Region of Interest (ROI) via camera or uploaded images, thereby potentially serving as a fast, practical, and easily accessible tool for the initial screening of anemia among adolescent girls in schools and primary health care facilities.
The Effect of Contrast Enhancement on Retinal Blood Vessel Segmentation Using CAS-UNet with Coordinate Attention Mardhatilla Al Haadiy, Hilya Zada; Anggraeny, Fetty Tri; Puspaningrum, Eva Yulia
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 21, No 1 (2026): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v21i1.26937

Abstract

Low contrast variation, uneven intensity distribution, and the presence of noise in retinal fundus images pose major challenges for blood vessel segmentation, particularly regarding thin and complex structures. These conditions make it difficult for models to accurately distinguish between blood vessels and the background. This study aims to analyze the impact of contrast enhancement techniques on retinal blood vessel segmentation performance using a CAS-UNet architecture modified with Coordinate Attention (CA). The methodology involves three preprocessing scenarios: Grayscale, Grayscale + CLAHE, and Grayscale + CLAHE + Gamma Correction. The model was trained using the DRIVE and CHASE_DB1 datasets with an 80:20 data split, an SGD optimizer, a learning rate of 0.01, and a combined BCE and Dice loss function over 50 epochs. Evaluation was conducted using a confusion matrix based on accuracy, sensitivity, specificity, F1-score, and IoU metrics. The results indicate that the Grayscale + CLAHE combination yielded the best performance—achieving a sensitivity of 81.46%, an F1-score of 81.63%, and an IoU of 69.01%—while also improving the detection of small blood vessels more consistently. These findings demonstrate that the appropriate application of contrast enhancement plays a crucial role in improving the quality of medical image segmentation.
Analysis of X and Threads Responses Based on Single Keywords Using Graph Neural Network Sinaga, Rifky Fahriza; Rizal, Rizal; Anshari, Said Fadlan
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 21, No 1 (2026): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v21i1.29143

Abstract

This study analyzes user responses on the social media platforms X and Threads regarding radicalism, based on the single keyword "radicalism." Differing interaction characteristics between the two platforms motivate a comparison of network structure using a graph-based approach and Graph Neural Networks (GNN). Data were collected through scraping of public content on X and Threads, with 5,000 raw posts each, yielding 1,327 reply interactions on X and 1,273 on Threads. Research stages included data collection, preprocessing, construction of the user-post graph, network metric analysis, and implementation of a Graph Autoencoder with a Graph Convolutional Network (GCN) encoder to generate node embeddings. The resulting graphs comprised 1,471 nodes and 1,223 unique edges for X, and 1,491 nodes and 1,081 unique edges for Threads, with X showing a denser structure (density 0.000565; average degree 1.663) than Threads (density 0.000487; average degree 1.450), while Threads was more fragmented (412 weak components versus 299 on X). The Graph Autoencoder was evaluated via link prediction using AUC and Average Precision (AP): X achieved AUC 0.5397 and AP 0.5879, slightly above the random-guessing baseline, while Threads achieved AUC 0.4987 and AP 0.5507, indicating a structure harder to reconstruct due to fragmentation. These quantitative results reinforce the network-metric findings that X forms a more connected network while Threads fragments into smaller groups. Practically, the findings offer an empirical basis for a decision-support system monitoring radicalism-related discourse, favoring dominant-cluster monitoring on X and parallel, cross-cluster monitoring on Threads. This study does not aim to detect or label accounts or content as radical, but to analyze interaction patterns and network characteristics of user responses.
Efficiency of Temporal Convolutional Networks in Karate Kata Evaluation: A Comparative Study Kaleb, Viona Zatil Aqmar; Utomo, Wiranto Herry
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 21, No 1 (2026): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v21i1.28888

Abstract

The objective quantification of complex martial-arts movement is a long-standing challenge in computer vision. This study reports a feasibility prototype that pairs MoveNet Lightning pose estimation with a lightweight Temporal Convolutional Network for two tasks on a small custom dataset of four foundational Karate Kata (Heian Shodan through Heian Yondan): (1) multi-class kata recognition and (2) binary correctness evaluation. A 132-dimensional kinematic feature vector is constructed per frame from 17 MoveNet keypoints, combining hip-centered normalized coordinates, confidence scores, joint angles, bone-length ratios, and end-effector velocities. The full dataset comprises 100 lateral-view videos (80 train/10 validation/10 test) collected from a single Indonesian dojo. A multi-task model with two causal-dilated 1D-convolution layers (32 filters, dilations 1 and 2) is trained for up to 100 epochs on Apple M1 hardware over five random seeds. The kata head reaches 100% validation accuracy with zero variance across all five seeds; however, this result is interpreted with strong reservations because the validation set contains only 10 samples. The correctness head consistently collapses to majority-class prediction (60.0% ± 0.0%, identical to the all-positive baseline of 6/10). The contribution of this paper is therefore a fully reproducible end-to-end pipeline (MoveNet → 132-D features → multi-task TCN → real-time webcam demo) together with a candid characterization of the dataset-driven limits that block the correctness task at this scale.

Page 30 of 30 | Total Record : 297

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