Garuda - Garba Rujukan Digital

Pengujian Integritas File Operasi Tanda Tangan Digital Menggunakan Kombinasi Hash MD5, RSA dan Skema Qr-Cod Hafiz Mursid; Julian Supardi; M. Qurhanul Rizkie
Generic Vol 14 No 2 (2022): Vol 14, No 2 (2022)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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

Kebijakan WFH pada masa pandemi COVID-19 mengakibatkan berbagai dokumen yang awalnya masih menggunakan sistem manual beralih ke sistem digital termasuk pada pengesahan pada dokumen tersebut. Maka penerapan tanda tangan digital dapat dijadikan alternatif sebagai bukti autentik sebuah dokumen untuk menggantikan tanda tangan konvensional. Penelitian ini bertujuan untuk mengembangkan perangkat lunak guna melakukan pengujian integritas dokumen dengan tanda tangan digital yang dibangun menggunakan fungsi hash MD5 dan algoritma RSA yang kemudian di-generate menjadi Qr-Code pada dokumen yang terdiri dari 1000 kata dengan ekstensi .docx. Dokumen yang akan diuji tersebut diberikan tanda tangan digital dengan perangkat lunak yang dibangun dan selanjutnya dilakukan pengujian integritas dengan melakukan operasi modifikasi terhadap file teks tersebut. Pada penelitian ini telah dibuat perangkat lunak untuk menerapkan skema autentikasi pada sebuah dokumen. Dari hasil penelitian yang dilakukan maka dapat disimpulkan jika fungsi hash MD5 dan algoritma RSA yang digenerate menjadi Qr-Code dapat diimplementasikan dengan baik untuk operasi tanda tangan digital.

Real Time Detection Of Waste Type Using Single Shot Multibox Detector Abdiansah Abdiansah; M. Qurhanul Rizqie; M. Hatta Aldino Ramadhan
Sriwijaya Journal of Informatics and Applications Vol 3, No 1 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i1.34

The lack of human initiative to manage their own wastes is one of many reasons why waste management in residential area is not optimal. A system to detect waste type in real time is a necessity to support the waste management process to be faster and optimal. This research propose the waste type detection systems using 2 types of Single Shot Multibox Detector models, SSD300 and SSD512. Both models were compared based on the accuracy and speed of detection on TACO dataset dan Waste Classification Data. SSD512 achieves a better accuracy of 0.63 mAP compared to the accuracy of SSD300, which is 0.57 mAP. Both models can also be said to be real time, with the SSD300's detection speed being faster at 51 fps compared to the SSD512's detection speed at 28 fps.

Automatic Language Identification for Indonesian-Malaysian Language Using Machine Learning Abdiansah Abdiansah; Muhammad Qurhanul Rizqie
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.21669

Language Identification (LID) aims to guess or identify which language the text or sound is coming from. Language identification tends to be easier in languages with different characteristics (e.g., Indonesian and English), but not for languages with similar characteristics (e.g., Indonesian and Malaysian). Similar languages can cause ambiguity that will be a bias for machine learning. Using Support Vector Machine (SVM) technique, this research tried to identify the Indonesian or Malaysian language. The training and testing data are taken from Leipzig Corpora Collection and Twitter dataset. The feature representation technique uses TF-IDF, and the baseline testing uses Naive Bayes Multinomial. We used two training techniques: split (20:80) and 10-cross validation. The experimental results show that the accuracy between the baseline and SVM is not too far. Both provide accuracy of around 90% and above. The results indicate that Indonesian and Malaysian language identification accuracy is relatively high even though using simple techniques.

Lung X-Ray Segmentation using Quadrant-Based Tracing Method Rizqie, Muhammad Qurhanul; Maolana, Iyus; Supriyanto, Eko
Generic Vol 16 No 1 (2024): Vol 16, No 1 (2024)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v16i1.182

Chest X-Ray is one of the most popular imaging modalities. Chest X-ray has been a subject of various imaging-related research for years. Among the various research, Lung segmentation is one of the most prominent ones. Nowadays the trend of research in segmentation is moving toward deep learning however traditional segmentation has advantage of requiring less calculation resources thus still has potential to be explored. In this paper an alternative non-deep learning segmentation method using graph-based method to trace border of the Chest X-Ray lung region is proposed. Chest X-Ray image was treated as a graph with coordinate of the pixels as vertex and value of the pixels as edges. First the image was divided into 4 quadrants, then the border of lung region on each quadrant was traced by finding the minimum spanning tree of the graphs on each quadrant, then the pixels recorded as the tree was smoothed and optimized using Savitzky-Golay filter. The results were analyzed using the confusion matrix by comparing the proposed method results with manual segmentation by a radiologist. The proposed method is successfully segment lung area on lateral view of chest X-Ray with an average accuracy of 0.936. Two sample T-test also employed in order to show that there is no significant difference between the proposed method results and manual segmentation by radiologist.

Adaptive Hint Generation for Educational Games Using Fuzzy Logic Anggina Primanita; Hadipurnawan Satria; Muhammad Qurhanul Rizqie; Ananda Haykel Iskandar; Wibisena Nugraha
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.41893

The increasing interest in programming education has led to a wide variety of learner abilities. However, existing learning media often remain fragmented, necessitating the development of adaptive tools to cater to learners of varying skill levels. This study employs fuzzy logic to generate dynamic hints for players struggling to solve programming challenges in an educational game. The effectiveness of the system was evaluated through both simulation and real-world experiments. Simulation results indicate that the fuzzy logic system successfully generates personalized hints, with the highest frequency of hints provided to beginner players. Real-world testing using the GUESS-18 framework demonstrated high playability and excellent usability scores for the game.

A Image Classification of Poisonous Plants Using the MobileNetV2 Convolutional Neural Network Model Method Muhammad Furqan Nazuli; Muhammad Fachrurrozi; Muhammad Qurhanul Rizqie; Abdiansah Abdiansah; Muhammad Ikhsan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4284

Poisonous plants can be dangerous for many people, but some can be used as medicines or as pest killers. Some people, especially those in environments with a wide variety of plants, can take advantage of this poisonous plant. Lack of knowledge and information causes the use of this poisonous plant to be inappropriate. This research aims to develop software to classify images of poisonous plants using the Convolutional Neural Network method with the MobileNetV2 model and to compare the accuracy of classification results with various dataset configurations and varying parameters. The research method used is a Convolutional Neural Network, which has relatively high accuracy in classifying various digital images. The data used in this research consists of eight poisonous plants and several non-poisonous plants. The research results on 153 test data show that the accuracy value was 99.34%, precision was 99%, recall was 99%, and F1-Score was 99%. This research contributes to developing software that can quickly provide information and knowledge about poisonous plants, offering a high-accuracy solution for classifying poisonous plants using image data. Furthermore, implementing MobileNetV2 provides an efficient and lightweight model suitable for deployment on mobile devices, enhancing accessibility and usability in the field. The potential applications of this software extend beyond individual use, potentially benefiting agricultural, medical, and educational sectors. Future work will expand the dataset to include more plant species and refine the model to improve its robustness against diverse environmental conditions and image qualities.

Sentiment and Emotion Classification Model Using Hybrid Textual and Numerical Features: A Case Study of Mental Health Counseling Ramayanti, Indri; Hermawan, Latius; Syakurah, Rizma Adlia; Stiawan, Deris; Meilinda, Meilinda; Negara, Edi Surya; Fahmi, Muhammad; Ghiffari, Ahmad; Rizqie, Muhammad Qurhanul
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.632

Mental health issues among individuals, particularly in counseling contexts, require practical tools to understand and address emotional states. This study explores the application of machine learning models for emotion detection in mental health counseling conversations, focusing on four algorithms: Bernoulli Naive Bayes, Decision Tree, Logistic Regression, and Random Forest. The dataset, derived from transcribed counseling sessions, underwent preprocessing, including stemming, stopword removal, and TF-IDF vectorization to create structured inputs for classification. Emotional categories such as "Depresi" (Depression), "Kecewa" (Dissapointed), "Senang" (Happy), "Bingung" (Confused) and "Stres" (Stress) were analyzed to evaluate model performance. Results indicated that Logistic Regression achieved the highest accuracy at 82%, showcasing its reliability and scalability, followed closely by Random Forest with 81%, demonstrating robustness in handling complex data structures. Bernoulli Naive Bayes performed competitively at 80%, excelling in computational efficiency, while Decision Tree recorded the lowest accuracy at 70%, reflecting its limitations in managing overlapping features and high-dimensional data. These findings highlight the potential of machine learning in addressing the increasing demand for scalable mental health support tools. The study underscores the importance of model selection, balanced datasets, and feature engineering to improve classification accuracy. Future work includes developing AI-driven chatbots for real-time emotion detection and integrating multimodal data to enhance interpretability. This research contributes to advancing automated solutions for mental health care, offering new pathways for timely and personalized interventions.

A Image Classification of Poisonous Plants Using the MobileNetV2 Convolutional Neural Network Model Method Nazuli, Muhammad Furqan; Fachrurrozi, Muhammad; Rizqie, Muhammad Qurhanul; Abdiansah, Abdiansah; Ikhsan, Muhammad
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4284

Poisonous plants can be dangerous for many people, but some can be used as medicines or as pest killers. Some people, especially those in environments with a wide variety of plants, can take advantage of this poisonous plant. Lack of knowledge and information causes the use of this poisonous plant to be inappropriate. This research aims to develop software to classify images of poisonous plants using the Convolutional Neural Network method with the MobileNetV2 model and to compare the accuracy of classification results with various dataset configurations and varying parameters. The research method used is a Convolutional Neural Network, which has relatively high accuracy in classifying various digital images. The data used in this research consists of eight poisonous plants and several non-poisonous plants. The research results on 153 test data show that the accuracy value was 99.34%, precision was 99%, recall was 99%, and F1-Score was 99%. This research contributes to developing software that can quickly provide information and knowledge about poisonous plants, offering a high-accuracy solution for classifying poisonous plants using image data. Furthermore, implementing MobileNetV2 provides an efficient and lightweight model suitable for deployment on mobile devices, enhancing accessibility and usability in the field. The potential applications of this software extend beyond individual use, potentially benefiting agricultural, medical, and educational sectors. Future work will expand the dataset to include more plant species and refine the model to improve its robustness against diverse environmental conditions and image qualities.

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