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All Journal JOIV : International Journal on Informatics Visualization JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Insearch: Information System Research Journal Jurnal Ilmiah Informatika dan Ilmu Komputer
Tanniewa, Adam M
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Social Sciences Other

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Pengembangan Sistem Informasi Monitoring Dana Desa Menggunakan Pendekatan Feature-Driven Development Tanniewa, Adam M; Nurnaningsih, Desi; Sulastri, Winda; Nugroho, Nurhasan
Insearch: Information System Research Journal Vol 4, No 02 (2024): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v4i02.9535

Abstract

Pemerintah Indonesia telah mengalokasikan dana desa untuk meningkatkan pembangunan di wilayah pedesaan. Namun, pengelolaan dana desa secara manual sering menghadapi berbagai masalah, seperti kesalahan pencatatan, waktu pengolahan yang lama, dan kurangnya transparansi. Untuk mengatasi masalah tersebut, penelitian ini mengembangkan Sistem Informasi Monitoring Dana Desa menggunakan pendekatan Feature-Driven Development (FDD). Metode FDD dipilih karena mampu mengembangkan perangkat lunak secara cepat dan terstruktur dengan fokus pada fitur-fitur spesifik yang bernilai bagi pengguna. FDD memungkinkan pengembangan perangkat lunak secara terstruktur dan iteratif, sehingga setiap fitur yang dihasilkan dapat langsung digunakan dan dievaluasi oleh pengguna. Penelitian ini menunjukkan bahwa implementasi FDD berhasil menyelesaikan pengembangan sistem dalam waktu singkat, yaitu 3 bulan dengan 6 iterasi. Sistem ini menyediakan fungsionalitas inti seperti pengelolaan dana masuk dan keluar, serta penyajian laporan dana desa. Evaluasi melalui usability testing menghasilkan skor rata-rata 86,25%, yang menunjukkan kesiapan sistem untuk implementasi praktis.
Analisis Perbandingan Metode AdaBoost, Gradient Boosting, dan XGBoost Untuk Kalsifikasi Status Gizi Pada Balita Erkamim, Moh.; Tanniewa, Adam M; AP, Irfan; Nurhayati, Nurhayati
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5717

Abstract

Nutritional issues in toddlers are a crucial issue that significantly impacts the health and development of children in Indonesia. Malnutrition can lead to various long-term health problems. Therefore, detecting and classifying the nutritional status of toddlers is very important. This study aims to analyze and compare boosting techniques to classify the nutritional status of toddlers, focusing on three boosting techniques: AdaBoost, Gradient Boosting, and XGBoost. This is done because boosting techniques work by sequentially building models, where each new model attempts to correct the prediction errors of the previous model. The results show that the XGBoost model provides the best performance with a precision of 0.9849, recall of 0.9848, accuracy of 0.9848, F1 score of 0.9848, and ROC-AUC of 0.9994 at an 80:20 data split ratio. Conversely, the AdaBoost model shows the lowest results with a precision of 0.6294, recall of 0.6292, accuracy of 0.6292, F1 score of 0.6291, and ROC-AUC of 0.7581 at a 90:10 data split ratio, caused by its sensitivity to outliers and noise in the data. These findings indicate that XGBoost is the best boosting model for classifying the nutritional status of toddlers, followed by Gradient Boosting, with AdaBoost in the last position. The outstanding performance of XGBoost is due to the use of regularization techniques, effective handling of missing values, and efficient and fast boosting algorithms through parallel processing techniques.
Pengembangan Model Klasifikasi Citra Penyakit Daun Lada Menggunakan Jaringan Syaraf Tiruan Learning Vector Quantization (LVQ) Sah, Andrian; Mulyadi, Mulyadi; Alexander, Allan Desi; Tanniewa, Adam M
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 4 No. 1 (2025): Volume 4 Nomor 1 March 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v4i1.53

Abstract

Lada (Piper nigrum) adalah komoditas pertanian bernilai tinggi, namun rentan terhadap penyakit daun akibat infeksi jamur, bakteri, atau hama. Identifikasi dini penting untuk mencegah penurunan hasil panen, namun metode konvensional berbasis observasi visual sering subjektif dan membutuhkan keahlian khusus. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan model klasifikasi penyakit daun lada menggunakan jaringan syaraf tiruan Learning Vector Quantization (LVQ) berbasis pengolahan citra digital. Proses penelitian dimulai dengan preprocessing, yang mencakup konversi ke ruang warna CIELAB untuk meningkatkan kontras, segmentasi menggunakan Otsu Thresholding, serta ekstraksi fitur warna dengan Mean Color dan fitur tekstur menggunakan Gray Level Co-occurrence Matrix (GLCM). Hasil ekstraksi fitur ini kemudian digunakan sebagai masukan untuk algoritma LVQ, yang melakukan klasifikasi berdasarkan pembelajaran vektor prototipe. Hasil evaluasi menunjukkan bahwa model LVQ yang dikembangkan mencapai tingkat akurasi keseluruhan sebesar 90,83%. Model menunjukkan performa terbaik dalam mengenali daun sehat dengan Precision, Recall, dan F1-Score sebesar 96,67%. Sementara itu, kelas Anthracnose memiliki Precision terendah sebesar 87,01%, dan kelas Leaf Blight menunjukkan Recall terendah sebesar 86,67% serta F1-Score terendah sebesar 88,14%. Meskipun terdapat variasi kinerja antar kelas, model ini terbukti efektif dalam menangani dataset terbatas, memiliki kemampuan klasifikasi yang baik terhadap data non-linear, serta memungkinkan interpretasi keputusan klasifikasi yang lebih jelas.
Kombinasi Metode Rank Order Centroid dan Additive Ratio Assessment Untuk Pemilihan Aplikasi Manajemen Inventaris Tanniewa, Adam M; Sah, Andrian; Kurniawan, Robi; Prayogo, M Ari
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.6347

Abstract

Selecting an appropriate inventory management application is a challenge for business actors, especially SMEs, due to the variety of features, costs, and complexities offered. Manual selection is often carried out without a clear systematic approach and tends to be influenced by bias, resulting in suboptimal decisions. This study aims to integrate the Rank Order Centroid (ROC) and Additive Ratio Assessment (ARAS) approaches in developing a Decision Support System (DSS) to determine the best inventory management application. ROC is used to assign proportional weights to criteria based on priority ranking, while ARAS evaluates alternatives using these weights and relative utility values against the ideal solution. The developed system includes key features such as data management for criteria, alternatives, and values, as well as the ability to generate recommendations through alternative ranking. Based on a case study, the best alternative identified is Sortly: Inventory Simplified, with the highest utility score of 0.8627, followed by Housebook - Home Inventory (0.8528), inFlow Inventory (0.8336), and Inventory Stock Tracker (0.7056). Usability testing showed an average user acceptance rate of 91%, categorized as "Excellent". The main contribution of this research is the implementation of a practical and efficient combination of ROC and ARAS for selecting inventory management applications. The findings can be adopted by businesses to support more accurate and efficient decision-making.
Improved Human Activity Recognition Using Stacked Sparse Autoencoder (SSAE) Algorithm Aziz, Firman; Mustamin, Nurul Fathanah; Rijal, Muhammad; Tanniewa, Adam M
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3079

Abstract

This study aims to enhance the performance of Human Activity Recognition (HAR) systems by implementing the Stacked Sparse Autoencoder (SSAE) algorithm combined with Support Vector Machine (SVM). The objective is to enhance the classification accuracy of human activities using sensor data. The materials for this study include a dataset collected from wearable devices equipped with accelerometers and gyroscopes. These devices generate time-series data representing a range of activities, such as walking, running, sitting, and standing. The raw data were preprocessed through normalization and segmented into fixed time windows to ensure uniformity and reliability for analysis. The methods utilized involve employing SSAE for automated feature extraction. The SSAE algorithm extracts hierarchical and abstract features from sensor data, enabling the model to learn complex patterns that traditional methods might overlook. The extracted features are then input into the SVM classifier to perform activity classification. SSAE was trained using unsupervised learning techniques, followed by supervised fine-tuning with labeled datasets. The results demonstrate that the SSAE-SVM model achieves superior performance compared to traditional SVM. The SSAE-SVM achieved 89% accuracy, 87% precision, 89% sensitivity, and 88% F1 score, significantly outperforming the traditional SVM’s 37% accuracy, 75% precision, 37% sensitivity, and 36% F1 score. These findings underscore the potential of SSAE in enhancing HAR systems by effectively extracting features from sensor data. Future research should focus on the real-time implementation of SSAE, leveraging diverse sensor modalities, and exploring its applicability in broader fields, such as predictive maintenance and personalized health monitoring.
EFEKTIVITAS SICERMAT (SISTEM INFORMASI CEK HARGA KOMODITAS) TERHADAP TRANSPARANSI HARGA KOMODITAS PERTANIAN DAN PERIKANAN DI SULAWESI BARAT Musyrifah, Musyrifah; Tanniewa, Adam M; S, Asmawati
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.8864

Abstract

Penelitian ini bertujuan untuk mengukur efektivitas sistem informasi SiCermat (Sistem Informasi Cek Harga Komoditas) dalam meningkatkan transparansi harga dan mendukung kemandirian ekonomi petani dan nelayan di Sulawesi Barat. Sistem ini dikembangkan berbasis web dengan antarmuka responsif yang menyajikan informasi harga komoditas pertanian dan perikanan secara real-time. Uji coba dilakukan terhadap 40 peserta yang terdiri dari petugas dinas, petani, dan nelayan di Kabupaten Majene dan Polewali Mandar dengan pendekatan kuantitatif deskriptif melalui observasi, wawancara, dokumentasi, dan kuisioner. Hasil pengujian menunjukkan bahwa terjadi peningkatan signifikan dalam akses informasi, dari 35% menjadi 90% setelah implementasi sistem, atau setara dengan peningkatan sebesar 157,14%. Pemahaman pengguna terhadap fluktuasi harga juga meningkat, ditunjukkan oleh peningkatan skor rata-rata dari 2,9 menjadi 4,9 (naik 28%). Selain itu, tingkat kepuasan pengguna terhadap sistem mencapai lebih dari 80% pada hampir semua indikator, dengan aspek kemudahan akses mencapai 90% dan manfaat ekonomi sebesar 88%. Temuan ini menunjukkan bahwa SiCermat efektif dalam mendukung keterbukaan informasi dan pemberdayaan ekonomi masyarakat di daerah terpencil, serta layak direplikasi di wilayah lain dengan permasalahan serupa.
Co-Authors Alexander, Allan Desi Firman Aziz Irfan AP M. Ari Prayogo Moh. Erkamim Mulyadi Mulyadi Musyrifah Musyrifah, Musyrifah Nurhasan Nugroho Nurhayati Nurhayati nurnaningsih, Desi Nurul Fathanah Mustamin Rijal, Muhammad Robi Kurniawan s, asmawati Sah, Andrian Sulastri, Winda