Abstract:Abstract: Ineffective drug demand management can lead to problems such as imbalanced drug distribution, excess stock, or shortages in community health centers. To address this, data mining can be utilized to support the…
planning and control process of drug inventory. Clustering techniques were chosen because they are able to group drug data based on certain characteristics, thus identifying stable and unstable drug supply patterns. This study aims to group drug data at Simpang Kawat Community Health Center in Jambi City, which can be used as a reference in planning drug needs in the next period. Data grouping is divided into three categories: slow-moving, medium-moving, and fast-moving. The research data includes attributes of drug name, initial stock, receipt, inventory, usage, and final stock, with a total of 1758 data sets, which were processed using the CRISP-DM framework through the RapidMiner application. Cluster quality evaluation was carried out using the Davies-Bouldin Index (DBI). The results showed that the K-Means algorithm obtained a DBI value of 0.175, smaller than K-Medoids which obtained a value of 0.354. Because a smaller DBI value indicates better cluster quality, K-Means provides more optimal clustering results than K-Medoids. Through these clustering results, community health centers can utilize drug cluster information to support more efficient drug procurement planning, as well as reduce the risk of excess or shortage of stock.
Keywords: data mining; clustering; k-means; k-medoids; davies-bouldin index
Abstract:Abstract: Recommendation systems are becoming increasingly important with the growth of streaming platforms. The purpose of this study is to compare the performance of Content-Based Filtering, Neural Collaborative Filtering,…
ing, and a combination of both in a movie recommendation system. The method used in this study involves retrieving movie details from the TMDB API and ratings from the MovieLens 32M Dataset (2010-2023). Each model's performance is evaluated using evaluation metrics such as RMSE and MAE. The results of this study indicate that Neural Collaborative Filtering achieves the best prediction performance (RMSE = 0.785423, MAE = 0.581262), followed by the hybrid model (RMSE = 0.800863, MAE = 0.660872), while Content-Based Filtering produces low performance and limits the capabilities of the hybrid model. In conclusion, these findings highlight the superiority of latent feature-based models such as NCF that learn directly from user interaction patterns over content-based approaches in the context of modern recommendation systems.
Keywords: content-based filtering; hybrid filtering; movie recommendation; neural collaborative filtering.
Abstrak: Sistem rekomendasi menjadi semakin penting seiring berkembangnya platform streaming. Tujuan dari penelitian ini adalah membandingkan kinerja Content-Based Filtering, Neural Collaborative Filtering dan kombinasi keduanya dalam sistem rekomendasi film. Metode yang digunakan dalam penelitian ini melibatkan pengambilan detail film dari TMDB API dan rating dari dataset MovieLens 32M Dataset (2010-2023). Setiap peforma model dievaluasi dengan menggunakan metrik evaluasi seperti RMSE dan MAE. Hasil dari penelitian ini menunjukkan bahwa Neural Collaborative Filtering mencapai kinerja prediksi terbaik (RMSE = 0.785423, MAE = 0.581262), diikuti oleh model hybrid (RMSE = 0.800863, MAE = 0.660872), sementara Content-Based Filtering menghasilkankan peforma yang rendah dan membatasi kemampuan model hybrid. Kesimpulannya, penelitian ini menyoroti superiotas model berbasis latent feature seperti NCF yang belajar langsung dari pola interaksi pengguna dibandingkan pendekatan berbasis konten dalam konteks sistem rekomendasi modern.
Kata kunci: content-based filtering; hybrid filtering; neural collaborative filtering; rekomendasi film.
Abstract:Giving awards is essential to motivate students; however, selecting outstanding students at the junior high school level is often conducted manually and subjectively, which can lead to unfairness and prolonged processing…
time. This study develops a Decision Support System (DSS) that integrates the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support objective and transparent student selection. A quantitative descriptive approach was employed, with data collected through questionnaires, interviews, and documentation at two state junior high schools in Banjarmasin City. Seven assessment criteria were applied: attendance, behavior, uniform neatness, extracurricular participation, academic grades, competition achievements, and disciplinary records. AHP was used to determine the weight of each criterion, while TOPSIS ranked students based on these weights. The web-based system was developed using PHP and MySQL and evaluated using the Technology Acceptance Model (TAM). Results show that academic grades had the highest weight (28.5%), followed by attendance (22.3%) and competition performance (15.2%). The TAM evaluation yielded average scores of 4.32 for Perceived Ease of Use, 4.40 for Perceived Usefulness, 4.15 for Attitudes Towards Use, and 4.28 for Behavioral Intention to Use. The DSS produces accurate rankings, is well-received by users, and offers an efficient, fair, and replicable solution for data-driven educational governance in the digital era.
Abstract:Abstract: The Ministry of Education, Culture, Research, and Technology, has developed guidelines for the Internal Quality Assurance System or known as SPMI, that is being implemented through the Internal Quality Audit (IQA)…
QA) with the PPEPP cycle, namely Determination (P), Implemen-tation (P), Evaluation (E), Control (P), and Improvement (P). Some universities have implemented IQA with system. The problem is that the system does not line well with the PPEPP cycle, which results in unsatisfactory audit results. The purpose of this study is to evaluate how well the university-owned AQI system features in line the PPEPP cycle and to highlight development opportunities. The method used Feature Oriented Domain analysis (FODA) and Acceptance Testing. This study delivered an analysis of IQA system features that consistent with PPEPP. The FODA results were validated by expert and tested with User Acceptance Test (UAT) with 89.98% user response that the system is acceptable. The research contributes to universities' understanding of the features necessary in the AQI system, which has an impact on the perfection of the university AQI system design in accordance with the PPEPP cycle.
Keywords: FODA; IQA system; PPEPP cycle; SPMI
Abstrak: Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi telah menyusun pedoman Sistem Penjaminan Mutu Internal atau yang dikenal dengan SPMI, yang diimplementasikan melalui Audit Mutu Internal (AMI) dengan siklus PPEPP, yaitu Penetapan (P), Pelaksanaan (P), Evaluasi (E), Pengendalian (P), dan Peningkatan (P). Beberapa perguruan tinggi telah mengimplementasikan AMI dengan sistem. Permasalahannya, sistem tersebut tidak sejalan dengan siklus PPEPP, sehingga hasil audit kurang memuaskan. Tujuan dari penelitian ini adalah untuk mengevaluasi seberapa baik fitur sistem AMI milik perguruan tinggi sejalan dengan siklus PPEPP dan menyoroti peluang pengembangan. Metode yang digunakan adalah analisis Feature Oriented Domain (FODA) dan Acceptance Testing. Penelitian ini menghasilkan analisis fitur sistem AMI yang konsisten dengan PPEPP. Hasil FODA divalidasi oleh ahli dan diuji dengan User Acceptance Test (UAT) dengan 89,98% respon pengguna bahwa sistem dapat diterima. Penelitian ini memberikan kontribusi terhadap pemahaman universitas terhadap fitur-fitur yang diperlukan dalam sistem AMI, yang berdampak pada kesempurnaan desain sistem AMI universitas sesuai dengan siklus PPEPP.
Kata kunci: FODA; siklus PPEPP; sistem AMI; SPMI
Abstract:Abstract: The inability to accurately monitor the lifespan of motorcycle batteries can lead to sudden failures, disrupt user activities, and increase maintenance costs. This issue is exacerbated by the absence of a predictive…
ctive system that can assist users and workshops in planning maintenance and managing battery inventory effectively. This study aims to develop a battery lifespan prediction model for motorcycles using the Double Moving Average (DMA) method. The model is built based on historical data from 12 motorcycle units, including usage frequency, duration, terrain conditions, and maintenance habits. Forecasting is conducted through two stages of moving averages followed by trend parameter calculations. Evaluation results show that the model has a high level of accuracy, with MAPE = 0.10, MAD = 1.68, and RMSE = 2.14, indicating very low prediction errors. In addition, DMA is also used to forecast product demand at PT Anugerah Karya Abiwara Kisaran to prevent stock shortages. The system is developed using Visual Studio 2010 and Microsoft Access and has proven effective in supporting maintenance planning and inventory control. With its high accuracy and efficiency, the results of this study provide tangible contributions to decision-making in battery maintenance and inventory management.
Keywords: battery; DMA; motorcycle; prediction.
Abstrak: Ketidakmampuan dalam memantau usia pakai aki sepeda motor secara akurat dapat menyebabkan kerusakan mendadak, mengganggu aktivitas pengguna, serta meningkatkan biaya perawatan. Permasalahan ini diperburuk oleh tidak tersedianya sistem prediktif yang membantu pengguna dan bengkel dalam merencanakan perawatan serta mengelola persediaan aki secara efisien. Penelitian ini bertujuan untuk mengembangkan model prediksi usia pemakaian aki sepeda motor dengan menggunakan metode Double Moving Average (DMA). Model dibangun berdasarkan data historis dari 12 unit sepeda motor yang mencakup frekuensi penggunaan, durasi, kondisi medan dan kebiasaan perawatan. Proses peramalan dilakukan melalui dua tahap perataan bergerak, yang kemudian diikuti dengan perhitungan parameter tren. Hasil evaluasi menunjukkan bahwa model ini memiliki tingkat akurasi yang tinggi, dengan nilai MAPE sebesar 0,10, MAD sebesar 1,68, dan RMSE sebesar 2,14, yang mengindikasikan tingkat kesalahan prediksi yang sangat rendah. Selain itu, metode DMA juga diterapkan untuk meramalkan permintaan produk pada PT Anugerah Karya Abiwara Kisaran guna mencegah terjadinya kekurangan stok. Sistem dikembangkan menggunakan Visual Studio 2010 dan Microsoft Access, serta terbukti efektif dalam mendukung perencanaan perawatan dan pengendalian persediaan. Dengan akurasi dan efisiensi yang tinggi, hasil penelitian ini memberikan kontribusi nyata dalam pengambilan keputusan terkait pemeliharaan aki dan manajemen inventori.
Kata kunci: baterai; DMA; prediksi; sepeda motor.
Abstract:Abstract: In the business world, supplier selection plays a crucial role in ensuring smooth company operations. Suppliers are responsible for providing raw materials with consistent quality, timely delivery, and competitive…
ive prices. The supplier selection process requires evaluation based on various criteria such as product quality, availability, packaging, price, and warranty. Currently, SNM Store places orders by contacting suppliers one by one via telephone to inquire about item availability. This method is time-consuming and may lead to delays in fulfilling item requirements. To address this issue, a Decision Support System (DSS) is needed to assist in efficiently determining the best supplier. One method that can be used in this system is MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis). MOORA is known to be effective in handling multi-criteria decision-making by simultaneously optimizing multiple objectives. This method also reduces subjectivity by assigning weights to each criterion and uses simple and fast calculations to evaluate the available alternatives. The objectives of this research are to identify the key criteria in supplier selection, apply the MOORA method in an efficient and user-friendly evaluation and selection process, and improve the operational efficiency of SNM Store in procurement so that item availability can be ensured in a timely manner.
Keywords: decision support system ; MOORA; supplier
Abstrak: Dalam dunia bisnis, pemilihan supplier memegang peranan penting dalam memastikan kelancaran operasional perusahaan. Supplier bertanggung jawab menyediakan bahan baku dengan kualitas konsisten, pengiriman tepat waktu, dan harga kompetitif. Proses seleksi supplier memerlukan evaluasi terhadap berbagai kriteria seperti kualitas produk, ketersediaan, pengemasan, harga, dan garansi. Toko SNM saat ini melakukan pemesanan dengan menghubungi supplier satu per satu melalui telepon untuk menanyakan ketersediaan barang. Metode ini memakan waktu dan dapat menyebabkan keterlambatan dalam pemenuhan kebutuhan barang. Untuk mengatasi hal tersebut, diperlukan sistem pendukung keputusan (Decision Support System) yang dapat membantu dalam menentukan supplier terbaik secara efisien. Salah satu metode yang dapat digunakan dalam sistem ini adalah MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis). MOORA dikenal efektif dalam menangani keputusan multi-kriteria dengan mengoptimalkan berbagai tujuan secara bersamaan. Metode ini juga mengurangi subjektivitas melalui pemberian bobot pada tiap kriteria dan menggunakan perhitungan yang sederhana serta cepat dalam mengevaluasi alternatif yang tersedia. adapun tujuan dari penelitian ini adalah untuk mengidentifikasi kriteria-kriteria penting dalam pemilihan supplier, menerapkan metode MOORA dalam proses evaluasi dan seleksi yang efisien dan mudah digunakan, serta meningkatkan efisiensi operasional Toko SNM dalam hal pengadaan barang agar ketersediaan barang dapat terjamin tepat waktu.
Kata kunci: MOORA; sistem penunjang keputusan; supplier;
Abstract:Abstract: This research is driven by the challenges faced by Universitas Lancang Kuning (UNILAK) in attracting applicants amidst intense competition, especially after the government's policy opened independent pathways to…
o State Universities (PTN) from 2022-2023, which impacted private university applicant numbers. To address this and support strategic planning, this study aims to predict the trend of prospective students applying to all study programs at UNILAK for the period 2025-2027. Two time series models were employed: ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory). Applicant data from 2019 to 2024 was used to build the model. The Augmented Dickey-Fuller (ADF) test confirmed the data's stationarity with a p-value of 0.0. ACF and PACF analyses determined the ARIMA parameters as p=1, d=1, q=1. The LSTM model was trained to capture more complex data patterns. ARIMA predictions for 2025, 2026, and 2027 are 3298.66, 3362.33, and 3371.30, respectively. LSTM predictions for the same years are 3335.64, 3476.52, and 3518.42. Evaluation using Root Mean Squared Error (RMSE) showed ARIMA (RMSE=588.72) to be more accurate than LSTM (RMSE=653.96). Nevertheless, LSTM provided a more optimistic prediction. This study concludes that ARIMA is better suited for short-term planning, while LSTM can be used for more ambitious long-term strategies.
Keywords: arima; LSTM; applicants; prediction; university
Abstrak: Penelitian ini didorong oleh tantangan Universitas Lancang Kuning (UNILAK) dalam menarik pendaftar di tengah persaingan ketat, khususnya setelah kebijakan pemerintah membuka jalur mandiri ke Perguruan Tinggi Negeri (PTN) sejak 2022-2023, yang menyebabkan penurunan jumlah pendaftar di universitas swasta. Untuk mendukung perencanaan strategis, studi ini bertujuan memprediksi tren jumlah calon mahasiswa yang mendaftar ke seluruh program studi di UNILAK untuk periode 2025-2027.Dua model deret waktu digunakan: ARIMA (AutoRegressive Integrated Moving Average) dan LSTM (Long Short-Term Memory). Data jumlah pendaftar dari 2019 hingga 2024 digunakan untuk membangun model. Uji Augmented Dickey-Fuller (ADF) menunjukkan data stasioner dengan p-value 0,0. Analisis ACF dan PACF menentukan parameter ARIMA sebagai p=1, d=1, q=1. Model LSTM dilatih untuk menangkap pola data yang lebih kompleks.Prediksi ARIMA untuk 2025, 2026, dan 2027 adalah 3298.66, 3362.33, dan 3371.30. Prediksi LSTM untuk tahun yang sama adalah 3335.64, 3476.52, dan 3518.42. Evaluasi menggunakan Root Mean Squared Error (RMSE) menunjukkan ARIMA (RMSE=588.72) lebih akurat daripada LSTM (RMSE=653.96). Meskipun demikian, LSTM memberikan prediksi yang lebih optimis. Studi ini menyimpulkan ARIMA lebih cocok untuk perencanaan jangka pendek, sementara LSTM dapat digunakan untuk strategi jangka panjang yang ambisius.
Kata kunci: arima; LSTM; pendaftar; prediksi; universitas
Abstract:Abstract: Face recognition based on deep learning has become an important technology in many areas. However, these systems often face challenges in real-world conditions, such as when the face is partially covered by accessories…
essories such as masks or glasses. This study aims to evaluate the effect of data augmentation by adding facial accessories (masks, glasses, and a combination of both) and geometric augmentation on the accuracy of face recognition systems. There are three types of datasets used in this method: the original dataset (category 1), the dataset with facial accessories augmentation (category 2), and the dataset with geometric augmentation (category 3). Data augmentation was performed on the training dataset to increase diversity, followed by the face detection process using SCRFD and feature extraction with ArcFace. The model was then trained using Multi-Layer Perceptron (MLP). Based on the results, adding face accessories (category 2) made the model a lot more accurate, hitting 99% accuracy. In category 3, adding geometric features improved accuracy to 91%. Other evaluation metrics, such as precision, recall, and F1-score, also showed improvement after augmentation. This study concludes that facial accessories augmentation is more effective in improving the accuracy and robustness of face recognition models compared to geometric augmentation.
Keywords: augmentation; deep learning; face recognition; glasses.
Abstrak: Pengenalan wajah berbasis deep learning telah menjadi salah satu teknologi penting dalam berbagai aplikasi. Namun, sistem ini sering kali menghadapi tantangan dalam kondisi dunia nyata, seperti saat wajah tertutup sebagian oleh aksesori seperti masker atau kacamata. Penelitian ini bertujuan untuk mengevaluasi pengaruh augmentasi data dengan menambahkan aksesori wajah (masker, kacamata, dan kombinasi keduanya) serta augmentasi geometris terhadap akurasi sistem pengenalan wajah. Metode yang digunakan melibatkan tiga kategori dataset: dataset asli tanpa augmentasi (kategori 1), dataset dengan augmentasi aksesoris wajah (kategori 2), dan dataset dengan augmentasi geometris (kategori 3). Augmentasi data dilakukan pada dataset pelatihan untuk meningkatkan keberagaman, diikuti dengan proses deteksi wajah menggunakan SCRFD dan ekstraksi fitur dengan ArcFace. Model kemudian dilatih menggunakan Multi-Layer Perceptron (MLP). Hasil penelitian menunjukkan bahwa augmentasi aksesoris wajah (kategori 2) memberikan peningkatan signifikan pada akurasi model, mencapai 99%, sedangkan kategori 3 dengan augmentasi geometris mencapai akurasi 91%. Metrik evaluasi lainnya, seperti precision, recall, dan F1-score, juga menunjukkan peningkatan setelah augmentasi. Penelitian ini menyimpulkan bahwa augmentasi aksesoris wajah lebih efektif dalam meningkatkan akurasi dan ketahanan model pengenalan wajah dibandingkan dengan augmentasi geometris.
Kata kunci: augmentasi; deep learning; kacamata; pengenalan wajah.
Abstract: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…
dy 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.
Abstract:Abstract: Mosques are places of jamaah and centers of social and economic activity for Muslims. Still, in the process of managing their activities, DKM administrators often face challenges related to efficiency, integration,…
ion, and transparency of information. This research aims to develop a web-based Mosque Management Information System (SIMMAS) that integrates the management of ZISWAF (zakat, infaq, sadaqah, waqf), qurban, inventory, activity information, and digital payments via QRIS. Using the Rapid Application Development (RAD) method, the system was designed to be developed quickly and in alignment with user needs. The evaluation was conducted using the Customer Satisfaction Index (CSI) approach, involving 60 respondents comprising mosque administrators and congregants. The results show that all SIMMAS services received CSI scores above 85%, indicating a high level of user satisfaction, particularly with the religious lecture scheduling feature. Nevertheless, there remains room for improvement, especially in the responsiveness and empathy aspects of financial and inventory services. This research is expected to serve as a foundation for the continued development of SIMMAS to become more effective, efficient, and digitally integrated in mosque management.
Keywords: user satisfaction; mosque information system; CSI; SIMMAS; ZISWAF
Abstrak: Masjid merupakan tempat ibadah, pusat aktivitas sosial dan ekonomi umat Islam, namun dalam proses pengelolaan kegiatannya, pengurus dkm kerap menghadapi tantangan terkait efisiensi, integrasi, dan transparansi informasi Penelitian ini bertujuan untuk mengembangkan Sistem Informasi Manajemen Masjid (SIMMAS) berbasis web yang mengintegrasikan pengelolaan ZISWAF, qurban, inventaris barang, informasi kegiatan, serta pembayaran digital melalui QRIS. Dengan menerapkan metode Rapid Application Development (RAD), sistem ini dirancang agar dapat dikembangkan secara cepat dan sesuai dengan kebutuhan pengguna. Evaluasi dilakukan menggunakan pendekatan Customer Satisfaction Index (CSI), yang melibatkan 60 responden dari kalangan pengurus dan jamaah masjid. Hasil evaluasi menunjukkan bahwa seluruh layanan SIMMAS memperoleh nilai CSI di atas 85%, mencerminkan tingkat kepuasan yang tinggi, khususnya pada fitur jadwal kajian. Meski demikian, masih terdapat ruang untuk perbaikan, terutama pada aspek responsivitas dan empati dalam layanan keuangan dan pengelolaan inventaris. Penelitian ini diharapkan dapat menjadi dasar pengembangan berkelanjutan SIMMAS dalam mendukung tata kelola masjid yang lebih efektif, efisien, dan berbasis digital.
Kata Kunci: kepuasan pengguna; sistem informasi masjid; CSI; SIMMAS; ZISWAF