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All Journal KUMARA CENDEKIA Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Jurnal Pendidikan Dasar Nusantara Mozaik Humaniora Jurnal Sejarah Citra Lekha SHAHIH : Journal of Islamicate Multidisciplinary Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pilar Nusa Mandiri EKONOMIS : Journal of Economics and Business Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL PENDIDIKAN TAMBUSAI PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal Masyarakat dan Budaya Pusaka : Jurnal Khazanah Keagamaan Proceedings of International Conference on Da'wa and Communication JISA (Jurnal Informatika dan Sains) JMK Jurnal Manajemen dan Kewirausahaan Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Abditeknika - Jurnal Pengabdian Kepada Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Simki Postgraduate MANUSKRIPTA Marifah: Jurnal Pendidikan dan Pemikiran Peradaban Islam
Samidi Samidi, Samidi
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Analisis Metode Load Data Ke Dalam Database Oracle 21c: Studi Pada Data Central Mapper Kemenkeu Utama, Aditio Pra; Iqbal Anshori, Muhammad; Samidi, Samidi
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 4 (2025): JPTI - April 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.733

Abstract

Dalam era informasi saat ini, pengelolaan dan analisis data menjadi aspek krusial yang mendukung pengambilan keputusan yang efektif. Kementerian Keuangan (Kemenkeu) memiliki kebutuhan untuk mengelola data yang akurat dan terstruktur guna mendukung berbagai fungsi keuangan dan administrasi. Aplikasi Central Mapper digunakan untuk sinkronisasi data penyaluran yang terhubung dengan registri penerima manfaat, memainkan peran penting dalam mengelola integrasi data bantuan sosial, memfasilitasi konfirmasi, verifikasi, dan validasi data dari kementerian/lembaga (K/L), mencocokkan data yang diterima dari pemilik program K/L dengan data dari lembaga penyalur, serta menjadi sumber data untuk dasbor. Penelitian ini berfokus pada perbandingan tiga metode pemuatan data umum ke dalam database Oracle: SQL*Loader, External Table, dan Pentaho Data Integration. Penelitian ini bertujuan untuk membandingkan metode-metode tersebut dari segi kecepatan, penggunaan resource, dan akurasi data, dengan menggunakan data Central Mapper Kemenkeu sebagai studi kasus. Tujuan akhirnya adalah memberikan rekomendasi metode terbaik untuk memuat data ke dalam database Oracle, sehingga dapat meningkatkan efisiensi proses bisnis dan pengambilan keputusan. Penelitian melibatkan persiapan lingkungan pengujian, persiapan data uji, implementasi masing-masing metode pemuatan, dan pengukuran waktu eksekusi, penggunaan sumber daya CPU dan memori, serta akurasi data. Hasil penelitian menunjukkan bahwa SQL*Loader dengan opsi "direct" dan "unrecoverable" menawarkan performa terbaik untuk volume data besar. Metode External Table dan Pentaho Data Integration juga menunjukkan performa baik untuk ukuran data yang lebih kecil. Penelitian ini mengidentifikasi kelebihan dan kekurangan masing-masing metode, memberikan wawasan berharga bagi Kemenkeu dan institusi serupa dalam memilih metode pemuatan data yang paling sesuai dengan kebutuhan operasional mereka.
Optimasi Performa Query Subsidi Debitur dengan Index and Table Partition, Subquery and Indexing, dan Parallel Query Execution Firmansyah, Eko; Firdaus, Hanafi; Samidi, Samidi
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 6 (2025): JPTI - Juni 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.824

Abstract

Pertumbuhan data yang pesat menyebabkan turunnya performa perhitungan subsidi debitur pada Sistem Informasi Kredit Program (SIKP). Waktu proses selama 8 jam membuat layanan SIKP terganggu karena pemangku kebijakan tidak dapat mencetak laporan. Penelitian ini bertujuan untuk mengoptimalkan perhitungan subsidi debitur dengan metode optimasi query. Metode yang akan digunakan mencakup Index and Table Partition Subquery and Indexing, dan Parallel Query Execution. Pengujian dilakukan secara eksperimen menggunakan 300 juta sampel data subsidi debitur Bank Rakyat Indonesia (BRI) tahun 2022. Hasil penelitian menunjukan bahwa sebelum menggunakan metode optimasi, sistem membutuhkan waktu rata-rata 7.992,34 detik untuk menyelesaikan 1 siklus perhitungan data sampel. Index and Table Partition menghasilkan waktu rata-rata 7.097,01 detik. Subquery and Indexing menghasilkan waktu rata-rata 3.270,33 detik. Parallel Query Execution menghasilkan waktu rata-rata 5.923,67 detik. Hasil optimal diperoleh ketika ketiga metode dikombinasikan, menghasilkan waktu rata-rata 3.038 detik, yaitu 61,98% lebih efisien dibandingkan metode yang ada. Penerapan metode optimasi query ini secara langsung dapat meningkatkan efisiensi SIKP dan operasional pemangku kebijakan tidak terganggu. Hasil penelitian ini memberikan solusi optimasi query kombinasi untuk pengolahan data besar.
Comparison of the RFM Model's Actual Value and Score Value for Clustering Samidi, Samidi; Suladi, Ronal Yulyanto; Kusumaningsih, Dewi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5416

Abstract

Clustering algorithms and Recency-Frequency-Money (RFM) models are widely implemented in various sectors of e-commerce, banking, telecommunications and other industries to obtain customer segmentation. The RFM model will assess a line of data which includes the recency and frequency of data appearance, as well as the monetary value of a transaction made by a customer. Choosing the right RFM model also influences the analysis of cluster results, the output of cluster results is more compact for the same clusters (inter-cluster) and separate for other clusters (intra-cluster). Through an experimental approach, this research aims to find the best data set transformation model between actual RFM values and RFM scores. The method used is to compare the actual RFM value model and the RFM score and use the silhouette score value as an indicator to obtain the best clustering results using the K-Means algorithm. The subject of this research is a stall-based e-Commerce application, where data was taken in the Wiradesa area, Central Java. The resulting data set consisted of 273,454 rows with 18 attributes from January 2022 to December 2022 by collecting historical data from shopping outlets to wholesalers. The analysis of the data set was carried out by transforming the data set using the RFM method into actual values and score values; then the dataset was used to obtain the best cluster data. The results of this research show that transaction data based on time (time series) can be transformed into data in the RFM model where the actual value is better than the RFM score model with a silhouette score = 0.624646 and the number of clusters (K) =3. The results of the clustering process also form a series of data with a cluster label, thus forming supervised learning data.
PROJECT MANAGEMENT OF STEEL PLATE WAREHOUSE INVENTORY INFORMATION SYSTEM Samidi, Samidi; Romadhan, Fitrah
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6258

Abstract

Information system project management is an activity of available resources from an information system solution development project so that a system solution can be produced that meets predetermined objectives. From the findings in the field that the process of applying the inventory information system project management is still constrained because the previous business process lacks support and some still use ms. office excel in recording the process of entering and exiting goods becomes an obstacle if many transactions occur in a day, and inventory data tends to have differences from the warehouse and head office, so this study aims to apply the inventory information system project management that has been developed using the waterfall development method can function optimally and effectively by implementing the Project Management Body of Knowledge (PMBOK) method where the focus of discussion is work breakdown structure analysis, activity of arrow analysis, and project cost estimate analysis. The results of this study obtained the results of stage-based WBS analysis, activity of arrow analysis with 58 days, while project cost estimate analysis with 14% for the communication stage, 20% for the planning stage, 57% for the modeling stage, 4% for the construction stage, 5% deployment stage.
Perbandingan Kinerja Teknik Index Bitmap dan B-Tree dalam Optimasi Query pada Database Oracle putra, adhitya eka; Samidi, Samidi
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 2 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i2.84906

Abstract

Penelitian ini bertujuan untuk mengevaluasi efektivitas teknik indexing dalam pengolahan data besar, khususnya dalam sistem basis data Oracle. Fokus utama penelitian ini adalah membandingkan dua teknik indexing yang paling umum digunakan, yaitu Index Bitmap dan B-Tree, untuk mengukur kinerja mereka dalam hal waktu eksekusi, penggunaan sumber daya (memori dan CPU), serta akurasi hasil pencarian. Eksperimen dilakukan dengan menggunakan dataset besar yang berasal dari sistem Revenue Accounting System (RAS) milik Direktorat Jenderal Pajak, yang mencakup lebih dari 600 juta baris data. Metode eksperimen yang digunakan melibatkan pembuatan kedua indeks pada tabel yang besar, diikuti dengan pengujian berbagai jenis query, seperti seleksi, agregasi, dan rentang data. Hasil penelitian menunjukkan bahwa Index Bitmap lebih efisien untuk query seleksi pada kolom dengan kardinalitas rendah, sementara B-Tree lebih unggul untuk query agregasi dan rentang data pada kolom dengan kardinalitas tinggi. Penggunaan memori juga menunjukkan perbedaan signifikan, dengan Index Bitmap lebih hemat memori, sedangkan B-Tree membutuhkan lebih banyak memori, tetapi lebih efisien pada operasi yang lebih kompleks. Temuan ini memberikan panduan praktis bagi pengembang aplikasi dan administrator basis data dalam memilih teknik indexing yang sesuai dengan jenis data dan query yang dihadapi. Hasil penelitian ini juga membuka peluang untuk penelitian lebih lanjut dalam pengujian teknik indexing pada platform basis data lain dan kondisi yang lebih beragam.
Optimasi Kueri pada Database Oracle Melalui Indeks dan Partisi Tabel untuk Data Besar Sugiarto, Bambang; Bagusputra, Argan Imam; Samidi, Samidi
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.862

Abstract

Mengoptimalkan kinerja database sangat krusial di era dominasi data. Pertumbuhan data eksponensial, khususnya pada data Kredit Usaha Rakyat (KUR) sejumlah 227.587.131 baris di database Oracle yang digunakan dalam penelitian ini, menjadi tantangan utama. Eksekusi kueri SQL yang lambat menghambat efisiensi operasional. Penelitian ini menerapkan strategi optimasi kinerja database Oracle melalui teknik pengindeksan (indeks tunggal dan komposit) dan partisi tabel berdasarkan rentang (kolom tahun). Kedua teknik ini bertujuan mempercepat pengambilan data dan meningkatkan efisiensi akses pada tabel besar. Tujuan penelitian adalah mengoptimalkan eksekusi kueri SQL pada data KUR yang besar tersebut. Evaluasi dilakukan dengan membandingkan waktu respons kueri pada empat skenario tabel (tanpa indeks, indeks tunggal, indeks komposit, serta indeks komposit dengan partisi). Hasil evaluasi menunjukkan bahwa penerapan indeks (tunggal dan komposit) serta partisi tabel secara umum meningkatkan kinerja kueri seleksi dan join secara signifikan dibandingkan tanpa optimasi, dengan waktu tercepat dicapai pada tabel berpartisi dengan penyebutan partisi eksplisit (0,082 detik untuk seleksi sederhana). Namun, untuk kueri agregasi, tabel tanpa indeks memberikan waktu respons lebih cepat (58.100 detik) dibandingkan tabel dengan indeks tunggal (71.700 detik) ataupun kombinasi indeks komposit dan partisi.
Data-Driven Approach to Managing Best-Selling Beauty Categories: Price, Rating, Review, and Stock Diroatmodjo, Indah Safira; Samidi, Samidi
JMK (Jurnal Manajemen dan Kewirausahaan) Vol 10 No 3 (2025): September
Publisher : Universitas Islam Kadiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32503/jmk.v10i3.7810

Abstract

The beauty industry in Indonesia is experiencing rapid growth, particularly through e-commerce platforms like Tokopedia. Many businesses still rely on intuition for product management, including decisions related to stock and pricing. This study develops a machine learning-based classification model to identify beauty products with high sales potential on Tokopedia, considering factors such as price, rating, review count, and stock availability. Ten classification algorithms are applied, including Naive Bayes, SVM, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, LightGBM, CatBoost, Extra Trees, and Multi-Layer Perceptron (MLP). The data is processed using Python on Google Colab. The results show that ensemble algorithms, particularly Random Forest, LightGBM, and Extra Trees, provide prediction accuracy above 91% and are highly effective in predicting best-selling products. Based on this model, businesses can optimize stock and pricing management to ensure that best-selling products are always available, thereby improving operational efficiency in a highly competitive market. This research offers a data-driven solution for more strategic and evidence-based product management on e-commerce platforms.
Harnessing Machine Learning to Decode YouTube Subscriber Dynamics: Regression Predictive Models and Correlations Mulyati, Sri; Samidi, Samidi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2084

Abstract

YouTube has grown and become a digital media giant. Content creators continue to struggle with predicting subscriber growth. Due to viewers' changing interests and the vast amount of information, it is challenging to determine which factors most influence subscription behavior. Optimizing content strategy and ensuring channel growth need an understanding of these traits. This study uses linear regression models (LR), neural networks (NN), and Gaussian processes (GP) to predict YouTube subscribers and examine category correlations using video data from various topics. The study of correlation matrix analysis was performed with an absolute root mean square error (RMSE) of 26256351, and the NN prediction model outperformed the LR and GP models. The correlation matrix indicates a slight positive correlation of 0.067 among the YouTube categories. Specifically, the correlation coefficients for population, unemployment rate, and urban population are 0.080, -0.012, and 0.082, respectively. These findings suggest future research to create more intentional content and search for significant factors that increase viewership and marketing audience growth.
Lightning Risk Mapping in West Sumatra using Kernel Density Estimation and Simple Additive Weighting Hardika, Deny; Eka Fauzy, Muhammad Alvy; Samidi, Samidi
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5571

Abstract

This study analyzes the spatial distribution, occurrence frequency, and lightning hazard vulnerability levels in West Sumatra during March 2024 using a geospatial and multi-criteria approach. By integrating meteorological data with land vulnerability assessments, the research applies Kernel Density Estimation (KDE) to map lightning strike density and employs Simple Additive Weighting (SAW) to incorporate land cover and socio-economic factors into the vulnerability evaluation. This combined approach produces a comprehensive lightning hazard risk map, identifying high-risk zones covering 3.01%, medium-risk zones 21.72%, low-risk zones 25.75%, and safe zones 49.52% of the total area. This innovative methodology represents a significant step forward in improving lightning risk management strategies and disaster mitigation policies, particularly in regions vulnerable to climate change and rapid urbanization. The findings not only highlight the importance of integrating spatial lightning data with environmental vulnerability assessments but also support practical applications in early warning systems and spatial planning to effectively minimize the impact of lightning hazards.
Pelatihan Kesenian Tradisional Pabitte Pasappu Untuk Pemertahanan Budaya Masyarakat Kajang di Desa Tanah Toa, Bulukumba, Sulawesi Selatan: The Traditional Performance of Pabitte Pasappu Training for Preservation of the Kajang Community Culture in Tanah Toa Village, Bulukumba, South Sulawesi Rabani, La Ode; Husain, Sarkawi B.; Samidi, Samidi; Khusyairi, Johny Alfian
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 9 No. 7 (2024): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v9i7.6990

Abstract

Local Society make the preservation of local arts and culture. One of the local communities that is the focus of community service is the Kajang indigenous community in Bulukumba, South Sulawesi. The Kajang community carries out its activities separately between Inner and Outer Kajang. Both spaces of communities have different standards in preserving one cultural art. The Inner Kajang community focuses on sacred traditional arts, while in the Outer Kajang community, the inherent traditions are profane. This community service is directed towards the goal of preserving/sustainability of the local culture of the Kajang community, which is being threatened by the influence of globalization, the influence of digital technology that enters the community, and the orientation of the Kajang Society which likes the world of modernity. As a result, sacred and profane traditional arts not attention, especially from the local younger generation. The method of community service is through training the younger generation of the Kajang community who come from the local community. Pabitte Pasappu Art Trainers come from local traditional leaders. This dedication succeeded in training the young generation of Kajang in an effort to maintain their art Pabitte Pasappu. The successful performance as shown on the YouTube channel of Prodi Ilmu Sejarah Unair became a benchmark for the success of this community service program, although in the not too distant future. The effectivity, time efficiency and intensity during training are key to achieving training targets.
Co-Authors AA Sudharmawan, AA Andharu, Dika Arnis Rachmadhani, Arnis Asmita, Maya Ayom Estu Royani, Ayom Estu Azida Batubara, Noor Bagusputra, Argan Imam Bambang Sugiarto Devit Setiono Dewi Kusumaningsih Dijerja, Gahara Diroatmodjo, Indah Safira Eddy Noviana Eka Fauzy, Muhammad Alvy Eka W, Samuel Yusach Eko Aprianto Fauzah, Titi Isnaini Firdaus, Hanafi Firmansyah, Eko Gatot Tri Pranoto Hardika, Deny Henni, Carmel Novella Henny Henny, Henny Iqbal Anshori, Muhammad Iqbal, Aldy Mercyano Johny Alfian Khusyairi Joko Daryanto La Ode Rabani Latif, Abd. Lilam Kadarin Nuriyanto, Lilam Kadarin Lusa, Sofian Minardi Minardi Moedjiono Moedjiono Nuryadi, Hepi Putra, Adhitya Eka Rachman, M Harviandi Rahmah, Yulinar Aini Romadhan, Fitrah Rosidin Rosidin Salsabila, Gina Sarkawi B Husain Setiadi, Indra Tresna Soepriadi, Raden Sri Mulyati Suladi, Ronal Yulyanto Syafran Nurrahman Syahdinullah S, Sutan Tjahjanto, Tjahjanto Utama, Aditio Pra Widyarto, Fajar