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Perancangan Sistem Pendukung Keputusan Metode Simple Additive Weighting pada Pemilihan Merk Personal Komputer di Laboratorium PPL SMKN 1 Garut Andriansyah Maulana; Sjahriani Datau; Andi Nurfadillah Ali
Jurnal Algoritma Vol 18 No 2 (2021): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.18-2.1030

Laboratorium kompetensi keahlian pengembangan perangkat lunak dan gim (PPL) merupakan laboratorium yang berada di bawah kompetensi keahlian pengembangan perangkat lunak dan gim (PPL) SMKN 1 Garut. Laboratorium PPL memiliki personal komputer (PC) sejumlah 40 PC, yang dilakukan rencana penambahan personal komputer atas usul dari ketua kompetensi pengembangan perangkat lunak dan gim dengan beberapa opsi merk personal komputer. Penelitian ini memakai model manajemen perancangan suatu metode Simple Additive Weighting (SAW) dengan memakai model-model yang digunakan sebagai pedoman untuk pengambilan keputusan antara lain harga, nilai ketahanan produk, nilai spesifikasi hardware dan nilai fleksibilitas produk. Penelitian ini menggunakan metode sistem pendukung keputusan yaitu Simple Additive Weighting dengan metodologi pengembangan sistem menggunakan metode prototyping. Hasil proses analisa berupa data personal komputer dari berbagai merk, dengan adanya sistem pendukung keputusan diharapkan dapat membantu ketua kompetensi keahlian pengembangan perangkat lunak dan gim dalam mengambil keputusan untuk memilih merk personal komputer yang terbaik untuk Laboratorium PPL SMKN 1 Garut.

Perancangan Sistem Pendukung Keputusan Metode Simple Additive Weighting pada Pemilihan Merk Personal Komputer di Laboratorium PPL SMKN 1 Garut Andriansyah Maulana; Sjahriani Datau; Andi Nurfadillah Ali
Jurnal Algoritma Vol 18 No 2 (2021): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.18-2.1030

Laboratorium kompetensi keahlian pengembangan perangkat lunak dan gim (PPL) merupakan laboratorium yang berada di bawah kompetensi keahlian pengembangan perangkat lunak dan gim (PPL) SMKN 1 Garut. Laboratorium PPL memiliki personal komputer (PC) sejumlah 40 PC, yang dilakukan rencana penambahan personal komputer atas usul dari ketua kompetensi pengembangan perangkat lunak dan gim dengan beberapa opsi merk personal komputer. Penelitian ini memakai model manajemen perancangan suatu metode Simple Additive Weighting (SAW) dengan memakai model-model yang digunakan sebagai pedoman untuk pengambilan keputusan antara lain harga, nilai ketahanan produk, nilai spesifikasi hardware dan nilai fleksibilitas produk. Penelitian ini menggunakan metode sistem pendukung keputusan yaitu Simple Additive Weighting dengan metodologi pengembangan sistem menggunakan metode prototyping. Hasil proses analisa berupa data personal komputer dari berbagai merk, dengan adanya sistem pendukung keputusan diharapkan dapat membantu ketua kompetensi keahlian pengembangan perangkat lunak dan gim dalam mengambil keputusan untuk memilih merk personal komputer yang terbaik untuk Laboratorium PPL SMKN 1 Garut.

PENERAPAN METODE DSS (MAUT & IRR) DALAM MENENTUKAN KELAYAKAN PENGADUAN Andi Nurfadillah Ali; Khaera Tunnisa
SINTECH (Science and Information Technology) Journal Vol. 5 No. 1 (2022): SINTECH Journal Edition April 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i1.952

The research and development center for mineral and coal technology or abbreviated as Puslitbang tekMIRA is located in Bandung, West Java, which has asset management, namely the rental of rooms or buildings. In asset management, there is a complaint page if a customer has a complaint. Based on the complaint data that came in at the same time, the authors saw that there were problems that were considered to need attention, namely which complaints would be resolved first and whether or not the complaint was feasible or not to be processed. This study uses the DSS (Decision Support System) method, namely Multi Attribute Utility Theory (MAUT) and IRR (Internal Rate of Return). The MAUT method is a method that converts some importance into a numerical value from a scale of 0-1 which represents the worst and best values and the final result is a ranking. While the IRR method is an indicator of the efficiency level of an investment that is used to provide an overview of whether the complaint is feasible or not to be followed up. The result of the application of this method is the feasibility of the complaint.

Perancangan Sistem Pendukung Keputusan Metode Simple Additive Weighting pada Pemilihan Merk Personal Komputer di Laboratorium PPL SMKN 1 Garut Andriansyah Maulana; Sjahriani Datau; Andi Nurfadillah Ali
Jurnal Algoritma Vol 18 No 2 (2021): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Laboratorium kompetensi keahlian pengembangan perangkat lunak dan gim (PPL) merupakan laboratorium yang berada di bawah kompetensi keahlian pengembangan perangkat lunak dan gim (PPL) SMKN 1 Garut. Laboratorium PPL memiliki personal komputer (PC) sejumlah 40 PC, yang dilakukan rencana penambahan personal komputer atas usul dari ketua kompetensi pengembangan perangkat lunak dan gim dengan beberapa opsi merk personal komputer. Penelitian ini memakai model manajemen perancangan suatu metode Simple Additive Weighting (SAW) dengan memakai model-model yang digunakan sebagai pedoman untuk pengambilan keputusan antara lain harga, nilai ketahanan produk, nilai spesifikasi hardware dan nilai fleksibilitas produk. Penelitian ini menggunakan metode sistem pendukung keputusan yaitu Simple Additive Weighting dengan metodologi pengembangan sistem menggunakan metode prototyping. Hasil proses analisa berupa data personal komputer dari berbagai merk, dengan adanya sistem pendukung keputusan diharapkan dapat membantu ketua kompetensi keahlian pengembangan perangkat lunak dan gim dalam mengambil keputusan untuk memilih merk personal komputer yang terbaik untuk Laboratorium PPL SMKN 1 Garut.

Sentiment Analysis of Bapenda South Sulawesi Mobile Application on Google Play Store Using Support Vector Machine Burhan, Muhammad Ikhwan; Ali, Andi Nurfadillah; Auliyah, A. Inayah; Hading, Muhaimin
Journal of Mathematics and Applied Statistics Vol. 2 No. 2 (2024): December 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i2.244

This study analyzes user sentiment toward the Bapenda Sulsel Mobile application, an e-government platform developed by the Regional Revenue Agency of South Sulawesi, Indonesia. The research aims to evaluate user feedback and identify areas for improvement to enhance user satisfaction. Using sentiment analysis, user reviews from Google Play Store were collected and classified into positive, negative, and neutral sentiments through the Support Vector Machine (SVM) algorithm. Preprocessing steps such as tokenization, stopword removal, and stemming were applied to prepare the data. Term Frequency-Inverse Document Frequency (TF-IDF) was used for feature extraction to enhance classification accuracy. The SVM model demonstrated an overall accuracy of 80%, achieving a high recall of 98% for positive reviews but only 40% for negative reviews, reflecting challenges in handling class imbalance. Results show that 72% of users expressed positive sentiment, praising the app’s functionality and ease of use. However, 28% of reviews were negative, citing issues like technical bugs and usability challenges The findings highlight the app’s strengths in delivering e-government services and its role in improving tax management. However, the significant proportion of negative feedback emphasizes the need for addressing user concerns. Recommendations include balancing the dataset, refining the SVM model, and prioritizing improvements based on user feedback. This study contributes to the broader understanding of applying sentiment analysis in evaluating e-government platforms and offers actionable insights for enhancing the user experience.

Comparative Analysis of Algorithms for Sensitive Outlier Protection in Privacy Preserving Data Mining Burhan, Muhammad Ikhwan; Ali, Andi Nurfadillah; Auliyah, A. Inayah; Hading, Muhaimin
Jurnal Tekno Kompak Vol 19, No 2 (2025): AGUSTUS (In Progress)
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v19i2.4754

Data mining is a crucial method in the realm of Big Data for extracting valuable predictive insights from extensive datasets. In the contemporary digital landscape, a significant difficulty is preserving individual privacy during data mining, particularly in safeguarding sensitive outliers that may harbour personal information. Outliers are data points that markedly diverge from the overall trend and frequently encompass very specialised or sensitive information. This paper examines the comparative efficacy of various clustering algorithms employed in outlier detection, specifically PAM (Partitioning Around Medoids), CLARA (Clustering Large Applications), CLARANS (Clustering Large Applications Based on Randomised Search), and ECLARANS (Enhanced CLARANS). This study aims to evaluate the efficacy of each algorithm in identifying outliers and to examine the usefulness of the employed privacy protection strategy, specifically the Gaussian Perturbation Random method. This experiment utilises two health datasets: the Diabetes Dataset from the National Institute of Diabetes and Digestive and Kidney Diseases and the Wisconsin Breast Cancer Dataset. The two datasets were chosen because to their multivariate features, which exhibit adequate data variation for outlier detection. The study's results indicate that the CLARA algorithm effectively identified a superior quantity of outliers compared to the other algorithms, with the diabetes dataset exhibiting the greatest count of outliers (65 outliers). The CLARA algorithm shown superiority in identifying outliers within extensive datasets due to the utilisation of a sampling methodology. Conversely, the PAM, CLARANS, and ECLARANS algorithms identified a same quantity of outliers in both datasets. ECLARANS shown superior time efficiency on the diabetic dataset, but CLARA demonstrated the highest efficiency on the breast cancer dataset. The Gaussian Perturbation Random technique was employed for preserving the identified sensitive outliers. The findings indicate that this strategy effectively maintains privacy while ensuring detection accuracy is not compromised. This method provides a dependable means of safeguarding individual privacy in health data mining, a domain characterised by significant privacy concerns.

Implementasi Pembelajaran Berbasis Kecerdasan Buatan Di Upt Sd Negeri 16 Parepare Hading, Muhaimin; Radhiansyah, Radhiansyah; Noor, Nurul Chairunnisa; syahruddin , A. Syahrinaldy; Auliyah, A. Inayah; Ali, Andi Nurfadillah; Burhan, Muhammad Ikhwan; Irsan, Muhammad
Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Vol. 6 No. 2 (2025): Volume 6, No 2 Juni 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/atjpm.v6i2.6311

Pemanfaatan teknologi dalam pendidikan dasar menjadi semakin krusial di tengah perkembangan era digital dan Revolusi Industri 4.0. Kegiatan pengabdian ini dilatarbelakangi oleh pentingnya peningkatan literasi teknologi di lingkungan sekolah dasar, khususnya dalam pemanfaatan kecerdasan buatan (Artificial Intelligence/AI) untuk mendukung proses belajar mengajar yang lebih interaktif dan adaptif. Program ini dilaksanakan di UPT SD Negeri 16 Parepare dengan tujuan utama untuk mengimplementasikan pendekatan pembelajaran berbasis AI. Metode pelaksanaan meliputi pelatihan intensif kepada guru mengenai konsep dan praktik penggunaan AI, penerapan langsung AI dalam kegiatan pembelajaran di kelas, serta evaluasi untuk mengukur dampak kegiatan. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan guru dalam mengintegrasikan AI ke dalam pembelajaran, serta meningkatnya minat dan partisipasi aktif siswa selama proses belajar. Kegiatan ini memberikan kontribusi positif dalam memperkenalkan transformasi digital di lingkungan sekolah dasar, serta berpotensi menjadi model replikasi untuk sekolah lainnya.

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