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All Journal Cyberspace: Jurnal Pendidikan Teknologi Informasi Jurnal Tekno Insentif Jurnal Ilmiah Wahana Bhakti Praja Journal of Electrical Engineering and Computer (JEECOM) Brilliance: Research of Artificial Intelligence J-SIGN (Journal Of Informatics, Information System, And Artificial Intelligence)
Maulyanda, Maulyanda
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Environmental Science Mathematics Other

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IMPLEMENTASI APLIKASI SISTEM INFORMASI PEMERINTAHAN DAERAH (SIPD) PADA DINAS TRANSMIGRASI DAN TENAGA KERJA KABUPATEN ACEH BARAT Nadya Balqis; Zuhrizal Fadhly; Maulyanda Az
Jurnal Ilmiah Wahana Bhakti Praja Vol 11 No 1 (2021)
Publisher : Lembaga Riset dan Pengkajian Strategi Pemerintahan IPDN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33701/jiwbp.v11i1.1953

Abstract

To create accurate information, the Ministry of Home Affairs designed an application for the Regional Government Information System (SIPD) as a useful application to achieve a successful coordination between the central government and regional governments. The implementation of SIPD is also aimed at the realization of good government, implementation of data technology-based regional planning and budgeting management. The implementation of SIPD is also to carry out the policies of each program of the West Aceh Regency Government activities, especially the Transmigration and Manpower Office. The case in this research is the implementation of SIPD at the Department of Transmigration and Manpower Kab. West Aceh. The object under study is the implementation of the SIPD application. The analytical model used in this research is descriptive qualitative analysis, using Edward III's theory of Communication, Human Resources, Disposition and Bureaucratic Structure. This theory is used to see where there are obstacles in the implementation of the Regional Government Information System (SIPD) application. The results of the analysis of the implementation of the Regional Government Information System (SIPD) application at the Transmigration and Manpower Office of the Regency. Aceh Barat from the four indicators of Edward III's theory covering the dimensions of Communication, Human Resources, Disposition and Bureaucratic Structure, there are still obstacles in the communication dimension; resource; and disposition. significantly, and there are obstacles in other dimensions, namely the time in using the SIPD application. Thus, Special Education and Training is needed to provide more understanding for SIPD application users, especially users in related agencies. Keywords: implementation, application, SIPD, Transmigration and Manpower Office, West Aceh
SIREUBOH: KLASIFIKASI DATA LOKASI BARANG MENGGUNAKAN REGION OF INTEREST (ROI) DAN ALGORITMA RANSAC Syafrial Fachri Pane; Rolly Maulana Awangga; Maulyanda Az
Jurnal Tekno Insentif Vol 12 No 2 (2018): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (396.503 KB) | DOI: 10.36787/jti.v12i2.98

Abstract

Abstrak - Perusahaan yang bergerak pada bidang logistik membutuhkan inovasi untuk meningkatkan daya saing dalam memberikan layanan terbaik mereka kepada konsumen, salah satunya pada Warehouse Management System (WMS) karena sistem tersebut masih kesulitan dalam mencocokkan data lokasi dengan sistem Logistics Execution System (LES) yang dipakai konsumen. sehingga pada bagian operation system management masih kesulitan dalam proses penempatan barang. Penelitian ini menggunakan algoritma RANSAC untuk mengukur keakuratan data lokasi barang pada proses penempatan barang yang sesuai, Region Of Interest (ROI) untuk memperkecil ruang lingkup data lokasi barang. Hasil analisis yang telah dilakukan dengan melakukan pencocokan data WMS dan LES didapatkan nilai persentase sebesar 87% untuk tingkat keakuratan data lokasi barang dengan mengolah 100 sample data lokasi barang yang dimiliki perusahaan. Hasil penelitian ini menunjukkan sangat bermanfaat karena dapat melakukan pencocokan data berdasarkan lokasi barang. Abstract - Companies that are engaged in logistics need innovation to improve competitiveness in providing their best services to consumers, one of which is the Warehouse Management System (WMS) because the system is still having difficulty matching location data with the Logistics Execution System (LES) system used by consumers. so that in the operation system management section there are still difficulties in the process of placing goods. This study uses the RANSAC algorithm to measure the accuracy of item location data in the process of placing the appropriate goods, Region of Interest (ROI) to reduce the scope of the location data of goods. The results of the analysis that have been done by matching WMS and LES data obtained a percentage value of 87% for the level of accuracy of the location data of goods by processing 100 samples of location data of goods owned by the company. The results of this study indicate that it is very useful because it can do data matching based on the location of the item.
Cryptography: Perancangan Middleware Web Service Encryptor menggunakan Triple Key MD5. Base64, dan AES Maulyanda Az; Syafrial Fachri Pane; Rolly Maulana Awangga
Jurnal Tekno Insentif Vol 15 No 2 (2021): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v15i1.497

Abstract

Penelitian ini membantu dalam melalukan proses Keamanan data atau informasi untuk menjamin kerahasiaan dan keaslian data atau informasi. Dalam perancangan ini menggunakan Kriptografi sebagai salah satu solusi dalam mengamankan data atau informasi. Metode kriptografi digunakan untuk mempersatukan algoritma Md5, Base64 serta AES (Advanced Encryption Standard). Kombinasi dari tiga algoritma menghasilkan ciphertext, yang dapat mengamankan data dari proses tag NFC. Penelitian ini menggunakan metodologi penelitian yang dapat menyatakan bahwa sistem yang dibangun dapat berfungsi dengan baik dan untuk keamanan nya aman digunakan, dari hasil penerapannya didapatkan hasil persentase keberhasilan 100%. Jadi, penelitian ini mampu menjawab permasalahan yang terjadi pada sistem keamanan data. Abstract This research helps in carrying out data or information security to ensure the confidentiality and authenticity of data or information. This design uses Cryptography as a solution in securing data or information. Cryptographic methods to unify the Md5, Base64, and AES (Advanced Encryption Standard) algorithms. The combination of the three algorithms produces ciphertext, which can secure data from the NFC tag process. This study uses a research methodology that can state that the system built can function correctly, and for security, it is safe to use because it has a 100% success percentage. So, this researchable to answer the problems that occur in the data security system.
Implementasi Middleware Pada Evomo Dengan Metode Web Service Restfull Dan Pengujian CI/CD, Coverage Serta Simulasi Protokol Grafana Syafrial Fachri Pane; Amri Yanuar; Alit Fajar Kurniawan; Maulyanda Az
Jurnal Tekno Insentif Vol 15 No 2 (2021): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v15i2.507

Abstract

ABSTRAK Penelitian ini membantu dalam melakukan proses analisis data monitoring dalam bentuk grafik, memanfaatkan CI/CD pipeline dalam melakukan implementasi CI/CD dapat memberikan kenyamanan dalam melakukan develop dan mengurangi bug, dari hasil implementasi CI/CD didapatkan hasil statements 93,33%, branch 100%, functions 87,88%, lines 94,92%. Grafik data disiapkan oleh grafana dalam bentuk script iframe diterapkan pada code program sistem. Penelitian ini menggunakan metodologi penelitian yang dapat menyatakan bahwa sistem yang dibangun dapat berfungsi dengan baik. Jadi, penelitian ini mampu menjawab permasalahan yang terjadi pada sistem Evomo. ABSTRACT This research helps in carrying out the monitoring data analysis process in graphical form, utilizing the CI/CD pipeline in implementing CI/CD can provide convenience in developing and reducing bugs. functions 87.88%, lines 94.92%, Graphic data prepared by grafana in the form of iframe script applied to the system program code. This study uses a research methodology that can state that the system built can function properly. So, this research is able to answer the problems that occur in the Evomo system.
Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption Maulyanda, Maulyanda; Deviani, Rini; Afdhaluzzikri, Afdhaluzzikri
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10875

Abstract

Protecting the privacy of medical data while enabling sophisticated data analysis is a critical challenge in modern healthcare. Fully Homomorphic Encryption (FHE) emerges as a powerful solution, enabling computations to be performed directly on encrypted data without exposing sensitive information. This study delves into the use of FHE for neural network inference in medical applications, investigating its role in safeguarding patient confidentiality while ensuring computational accuracy and efficiency. Experimental findings confirm the practicality of using FHE for medical data classification, demonstrating that data security can be preserved without significant loss of performance. Furthermore, the research explores the balance between computational overhead and model precision, shedding light on the complexities of deploying FHE in real-world healthcare AI systems. By emphasizing the significance of privacy-preserving machine learning, this work contributes to the development of secure, ethical, and effective AI-driven medical solutions.
Sentiment Analysis of Mental Health Using Support Vector Machine (SVM) with FastAPI Implementation Maulyanda, Maulyanda; Sri Azizah Nazhifah
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6580

Abstract

Mental health is a vital aspect that contributes significantly to an individual’s productivity, daily activity, and overall quality of life. With the increasing prevalence of mental health issues, early detection and analysis are essential. This study aims to identify mental health conditions using a machine learning approach, specifically the Support Vector Machine (SVM) algorithm. The dataset used consists of 53,043 text-based statements that are classified into seven distinct categories of mental conditions: normal, depression, suicide, anxiety, bipolar, stress, and personality disorders. The preprocessing steps applied to the dataset include text cleaning, tokenization, stopword removal, and lemmatization to standardize the textual input. Following this, 80% of the data is allocated for training the model, while the remaining 20% is reserved for testing purposes. The SVM algorithm is utilized to build a predictive model capable of classifying mental conditions based on text input. Furthermore, this model is deployed through an application interface using the FastAPI framework, enabling integration with digital platforms. The results indicate that the model achieves an accuracy of 79%, a recall of 77%, and an F1-score of 73%. These findings suggest that SVM is a capable and efficient method for analyzing and detecting various mental health conditions. This approach supports early intervention and offers practical implications for digital mental health screening tools.
Comparative Evaluation of NDVI-Based Vegetation Classification Using Rule-Based Thresholding and Random Forest Models Nazhifah, Sri Azizah; Maulyanda; Putri, Andriani; Kiftiyani, Usfita
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 9 No 2 (2025)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/agmq9j89

Abstract

This study aims to compare vegetation classification performance using NDVI derived from Sentinel-2A and Landsat 8 satellite imagery through two different approaches: rule-based classification and machine learning with the Random Forest algorithm. The rule-based approach applies a fixed NDVI threshold of 0.45 to distinguish vegetation and non-vegetation areas. In contrast, the Random Forest model was trained using 70% of the labeled data and tested on the remaining 30%, with NDVI values from both satellite sources as input features. The evaluation results show that the Random Forest model achieved perfect classification accuracy (100%). However, this may be due to using the same labeled dataset for both training and validation, which can lead to overfitting. On the other hand, the rule-based classification yielded an accuracy of 79.7%. This lower performance is likely caused by several factors, including the resolution differences between Sentinel-2 and Landsat 8 imagery, and the subjectivity involved in selecting the NDVI threshold value. The manual threshold setting may lead to bias and a higher number of misclassified pixels. Therefore, while rule-based methods are simple and interpretable, they are less robust. Machine learning approaches, such as Random Forest, offer more flexible and accurate classification when supported by properly separated training and validation datasets.
WorkoutLife: Penerapan Algoritma K-Nearest Neighbor (KNN) untuk Rekomendasi Workout Berdasarkan Data Gaya Hidup Maulyanda, Maulyanda; Deviani, Rini; Sabrina, Fathia; Afdhaluzzikri, Afdhaluzzikri
J-SIGN (Journal of Informatics, Information System, and Artificial Intelligence) Vol 3, No 2 (2025): November
Publisher : Department of Informatics, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/j-sign.v3i2.50115

Abstract

Current physical activity is no longer viewed merely as a bodily exercise but has become an essential part of the lifestyle. However, the diversity of available workout types often makes it difficult for individuals to determine the most suitable form of exercise based on their personal needs and lifestyle habits. This issue serves as the foundation of this research, which aims to develop a workout recommendation system based on lifestyle data using the K-Nearest Neighbor (KNN) algorithm. The results indicate that the KNN algorithm, with an optimal K value of 107, achieves an accuracy rate of 90% in recommending workout types. The High Intensity Interval Training (HIIT) and Yoga categories were identified as the most accurately recognized exercises by the model, with an F1-score of 95%. These findings demonstrate that the KNN method is effective in identifying lifestyle patterns and providing personalized workout recommendations. Therefore, the KNN-based recommendation system is expected to serve as an adaptive and intelligent solution to assist individuals in selecting workout types that best fit their lifestyles.
PERFORMANCE ANALYSIS OF MACHINE LEARNING AND INDOBERT IN CLASSIFYING SENTIMENTS ON INDONESIA'S FREE NUTRITIOUS MEAL Maulyanda; Nazhifah, Sri Azizah; Pane, Syafrial Fachri; Irvanizam, Irvanizam
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 10 No 1 (2026)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v10i1.33886

Abstract

Natural Language Processing (NLP) is a branch of artificial intelligence that is widely used to analyze whether a sentence contains positive, negative, or neutral sentiment, particularly in the context of expressing opinions in the online environment. This study compares several models to identify the most optimal one, namely Naïve Bayes, Support Vector Machine (SVM), XGBoost, and IndoBERT. The dataset used in this research was obtained from Kaggle and consists of 5,644 data points in the neutral class, 2,934 data points in the positive class, and 2,606 data points in the negative class. Prior to model implementation, the dataset underwent a preprocessing stage that included case folding, cleansing, tokenization, stemming, and stopword removal. Subsequently, the data were trained using the four aforementioned methods. The results indicate that Naïve Bayes achieved an accuracy of 75%, SVM reached 79%, XGBoost obtained 76%, while IndoBERT achieved the highest accuracy at 85%. Therefore, it can be concluded that, using this dataset, IndoBERT performed sentiment classification very effectively.
Co-Authors Afdhaluzzikri, Afdhaluzzikri Alit Fajar Kurniawan Amri Yanuar Deviani, Rini Irvanizam, Irvanizam Kiftiyani, Usfita Nadya Balqis Nazhifah, Sri Azizah Putri, Andriani Rolly Maulana Awangga, Rolly Maulana Sabrina, Fathia Sri Azizah Nazhifah Syafrial Fachri Pane Syafrial Fachri Pane, Syafrial Fachri Zuhrizal Fadhly