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Identifikasi Pola Seleksi Penentuan Calon Wali Nagari dengan Menggunakan Artificial Neural Network Algoritma Perceptron Yuhandri, Muhammad Habib; Mayola, Liga
Jurnal KomtekInfo Vol. 10 No. 4 (2023): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v10i4.485

Proses seleksi pemilihan calon Wali Nagari atau setingkat dengan Kepala Desa merupakan salah satu bagian dari sistem demokrasi yang ada saat sekarang ini. Pada dasarnya seleksi penentuan calon wali nagari tersebut berasal dari ketentuan yang dibuat oleh Komisi Pemilihan Umum (KPU) dan dieselengagarakan oleh Kelompok Penyelenggara Pemungutan Suara (KPPS). Adapun permasalahan yang sering terjadi pada saat seleksi penentuan calon yakni banyaknya para calon dan pendukung yang menyalahkan kinerja dari pihak KPPS, sehingga dapat menimbulkan krisis kepercayaan. Berdasarkan hal tersebut maka penelitian ini bertujuan untuk indentifikasi pola seleksi penentuan calon wali nagari dengan menggunakan Artificial Neural Network (ANN) algoritma Perceptron. Algoritma Perceptron pada dasarnya mampu melakukan identifikasi terhadap pola, aturan dan ketentuan dengan menggunakan variabel-variabel yang telah ditentukan. Proses penentuan nilai yang digunakan pada variabel tersebut nantinya akan memainkan peran logika fuzzy untuk memberikan nilai yang tepat beradasarkan data yang didapatkan sebelumnya. Hasil pengujian ANN dengan menggunakan algoritma perceptron pada proses pelatihan dan pengujian telah mampu menghasilkan keluaran yang tepat dan akurat. Hasil tersebut dapat dijadikan pola dalam melakukan seleksi penentuan calon wali nagari dan juga dijadikan sebagai basis knowlade based system. Berdasarkan hasil tersebut maka penelitian ini akan memberikan kontribusi untuk membantu kinerja KPPS untuk melakukan seleksi dalam menetapkan calon dalam pemilihan.

Sectoral vulnerabilities and adaptations to climate change: insights from a systematic literature review Prihandoko, Prihandoko; Windarto, Agus Perdana; Yanto, Musli; Yuhandri, Muhammad Habib
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6944-6957

Climate change is an urgent global issue impacting various life sectors, including health, agriculture, and infrastructure. This systematic literature review (SLR) aims to provide a comprehensive synthesis of research on sectoral vulnerabilities and adaptation strategies to climate change. Utilizing bibliometric analysis, the review identifies key themes and research gaps, highlighting the successes and challenges in implementing adaptation strategies. Key findings reveal that topics such as climate change, adaptive management, agriculture, public health, and food security are central to the research discourse. However, areas like health equity, sanitation, and agricultural worker adaptation remain under-researched. The analysis underscores the necessity for holistic, context-specific, and innovative approaches to policy-making, Scopus integrating sustainable development and public health to enhance resilience and adaptive capacity in vulnerable regions. This review offers valuable insights for researchers and policymakers aiming to develop effective adaptation strategies and address the multifaceted challenges of climate change.

Optimization of the Activation Function for Predicting Inflation Levels to Increase Accuracy Values Windarto, Agus Perdana; Rahadjeng, Indra Riyana; Siregar, Muhammad Noor Hasan; Yuhandri, Muhammad Habib
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7776

This study aims to optimize the backpropagation algorithm by evaluating various activation functions to improve the accuracy of inflation rate predictions. Utilizing historical inflation data, neural network models were constructed and trained with Sigmoid, ReLU, and TanH activation functions. Evaluation using the Mean Squared Error (MSE) metric revealed that the ReLU function provided the most significant performance improvement. The findings indicate that the choice of activation function and neural network architecture significantly influences the model's ability to predict inflation rates. In the 5-7-1 architecture, the Logsig and ReLU activation functions demonstrated the best performance, with Logsig achieving the lowest MSE (0.00923089) and the highest accuracy (75%) on the test data. These results underscore the importance of selecting appropriate activation functions to enhance prediction accuracy, with ReLU outperforming the other functions in the context of the dataset used. This research concludes that optimizing activation functions in backpropagation is a crucial step in developing more accurate inflation prediction models, contributing significantly to neural network literature and practical economic applications.

Optimization of the Activation Function for Predicting Inflation Levels to Increase Accuracy Values Windarto, Agus Perdana; Rahadjeng, Indra Riyana; Siregar, Muhammad Noor Hasan; Yuhandri, Muhammad Habib
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7776

This study aims to optimize the backpropagation algorithm by evaluating various activation functions to improve the accuracy of inflation rate predictions. Utilizing historical inflation data, neural network models were constructed and trained with Sigmoid, ReLU, and TanH activation functions. Evaluation using the Mean Squared Error (MSE) metric revealed that the ReLU function provided the most significant performance improvement. The findings indicate that the choice of activation function and neural network architecture significantly influences the model's ability to predict inflation rates. In the 5-7-1 architecture, the Logsig and ReLU activation functions demonstrated the best performance, with Logsig achieving the lowest MSE (0.00923089) and the highest accuracy (75%) on the test data. These results underscore the importance of selecting appropriate activation functions to enhance prediction accuracy, with ReLU outperforming the other functions in the context of the dataset used. This research concludes that optimizing activation functions in backpropagation is a crucial step in developing more accurate inflation prediction models, contributing significantly to neural network literature and practical economic applications.

Development of Signature Image Processing Using Shape and Texture Patterns Prihandoko; Rahmawati, Sri; Yuhandri, Muhammad Habib
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.635

A signature is a sign in written form, a person's identity for whether a document is correct or not, commonly known as a Biometric system. The Biometric system is the most basic, crucial and considered a superb process for a signature in detecting a person's identification and security. Signature forgery is a fraud that often occurs, causing bigger and longer expenses. For reasons like these, a signature detection system must be able to quickly and accurately recognize genuine and dummy signatures. The purpose of this study was to present the original and dummy signature pattern recognition by grouping the original signature data. In this study, Image Segmentation was used to divide the image into several parts, the K-Means Clustering algorithm to group several parts according to the properties of each object, and Feature Extraction of Texture Patterns and Shape Patterns with Gray Level Co-Occurrence Matrix (GLCM) to obtain feature values such as Entropy, Energy, Homogeneity, Correlation, and Contrast which has resulted in a study to detect genuine and counterfeit signatures. Preliminary results show that the percentage of identification of the signature biometric system developed using Feature Extraction with signature shapes on texture patterns got an average similarity rate of: 92.74%, and signature shapes on shape patterns attained an average similarity rate of: 79.20%. Therefore, the texture extraction pattern can detect the degree of similarity between the original signature and the dummy signature with a higher percentage value compared to the shape extraction pattern. The proposed method can produce better accuracy

EXPLANATION OF FEATURE EXTRACTION IN FACE RECOGNITION USING VIOLA JONES ALGORITHM Devita, Retno; Rianti, Eva; Yuhandri, Muhammad Habib; Putra, Ondra Eka
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 3 (2025): Juni 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3844

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 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.

Integrasi IoT dan Algoritma CatBoost untuk Deteksi Kualitas Udara Secara Real-Time di Wilayah Kota Padang Yuhandri, Muhammad Habib; Awal, Hasri
JSR : Jaringan Sistem Informasi Robotik Vol 9, No 2 (2025): JSR:Jaringan Sistem Informasi Robotik
Publisher : AMIK Mitra Gama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58486/jsr.v9i2.529

Penelitian ini mengembangkan sistem monitoring kualitas udara berbasis Internet of Things (IoT) dan algoritma machine learning CatBoost untuk mendeteksi tingkat pencemaran udara di Kota Padang secara real-time. Sistem ini menggunakan sensor Nova PM (PM2.5 dan PM10) dan MQ-7 (karbon monoksida/CO) yang dihubungkan ke mikrokontroler Wemos D1, lalu mengirimkan data secara otomatis ke Google Sheets sebagai basis penyimpanan. Data yang terkumpul diproses melalui tahapan preprocessing menggunakan StandardScaler, kemudian dilatih menggunakan model CatBoost. Hasil evaluasi menunjukkan bahwa model CatBoost mampu mengklasifikasikan lima kategori kualitas udara (Baik, Sedang, Tidak Sehat, Sangat Tidak Sehat, dan Berbahaya) dengan akurasi mencapai 95%, serta nilai precision dan f1-score rata-rata di atas 0.90. Sistem ini juga diimplementasikan dalam bentuk antarmuka pengguna (GUI) berbasis Streamlit yang menampilkan data sensor terkini dan hasil prediksi secara visual. Hasil penelitian ini menunjukkan bahwa integrasi IoT dan machine learning dapat menjadi solusi yang efektif dalam mendeteksi dan memantau kualitas udara secara real-time di kawasan perkotaan.

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