Development of an Integrated Raw Material Inventory Management Information System with a Food Menu for Profit and Loss Calculation Using the Rapid Application Development (RAD) Method (Case Study: Bento Kopi Pamulang)

2025-06-22T20:52:27+07:00

The use of information technology is a crucial factor in improving business efficiency, particularly in inventory management. Bento Kopi Pamulang still relies on manual recording of raw material inventory, which leads to various challenges such as data errors, information delays, and inaccurate COGS calculations. To address these challenges, this study developed a digital-based inventory management information system integrated with food menu data. The development was conducted using the Rapid Application Development (RAD) method, encompassing planning, design, prototype development, and implementation. Each stage was conducted iteratively with direct user involvement, from needs interviews and design validation to prototype trials and implementation evaluation. Active user involvement helped ensure the system met operational needs in the field. The resulting system was proven capable of recording raw material inflows and outflows in real time, automatically calculating COGS, and presenting faster and more accurate financial reports. System testing showed a 40% increase in recording efficiency and up to 95% increase in data accuracy compared to the manual system.

Abstrak

Pemanfaatan teknologi informasi menjadi faktor penting dalam meningkatkan efisiensi bisnis, terutama pada aspek pengelolaan persediaan. Bento Kopi Pamulang masih mengandalkan pencatatan manual untuk stok bahan baku, yang menyebabkan berbagai kendala seperti kesalahan data, keterlambatan informasi, dan perhitungan HPP yang tidak akurat. Untuk menjawab permasalahan tersebut, penelitian ini mengembangkan sistem informasi manajemen inventory berbasis digital yang terintegrasi dengan data menu makanan. Pengembangan dilakukan menggunakan metode Rapid Application Development (RAD), yang mencakup tahap perencanaan, desain, pengembangan prototipe, dan implementasi. Setiap tahap dilakukan secara iteratif dengan keterlibatan langsung dari pengguna, mulai dari wawancara kebutuhan, validasi desain, uji coba prototipe, hingga evaluasi implementasi. Keterlibatan aktif pengguna membantu memastikan sistem sesuai dengan kebutuhan operasional di lapangan. Sistem yang dihasilkan terbukti mampu mencatat keluar masuk bahan baku secara real-time, menghitung HPP secara otomatis, serta menyajikan laporan keuangan yang lebih cepat dan akurat. Pengujian sistem menunjukkan adanya peningkatan efisiensi waktu pencatatan sebesar 40%, dan akurasi data meningkat hingga 95% dibanding sistem manual.

Predicting Employment Status 6 Months After Graduation with Machine Learning Learning : A Comparative Study of 3,945 Indonesian Graduates

2025-08-10T08:29:55+07:00

The high unemployment rate of undergraduate graduates in Indonesia, reaching 11.4% in the first six months after graduation, indicates the need for an early prediction system to identify factors that influence student employability. This study aims to analyze and compare the performance of three machine learning algorithms (Random Forest, Logistic Regression, and XGBoost) to predict employment status 6 months after graduation based on academic and socioeconomic data. The dataset consists of 3,945 graduates from universities in Padangsidimpuan with variables of study program, study duration, GPA, gender, and parental income. The operational target is employment status 6 months after graduation (binary: employed = 1, not yet = 0) with the proportion of employed classes: 48.2 %, not yet: 51.8%. Evaluation uses stratified 5- fold cross-validation with accuracy metrics, balanced accuracy, F1- macro, ROC-AUC, and PR-AUC. Model interpretability is analyzed using permutation importance and SHAP values. Random Forest achieved the best performance with F1- macro 0.524±0.015, ROC-AUC 0.567±0.012, followed by Logistic Regression (F1- macro : 0.511±0.018) and XGBoost (F1- macro : 0.506±0.020). The majority baseline achieved an accuracy of 51.8 %. Permutation importance analysis identified GPA as the most influential factor (importance : 0.082), followed by parental income (0.067) and duration of study (0.041). The machine learning model provided a moderate improvement compared to the majority baseline. GPA and socioeconomic factors were shown to significantly influence graduates' employment status. These findings can support the development of an early warning system for data-based student mentoring.

Abstrak

Tingginya tingkat pengangguran lulusan sarjana di Indonesia mencapai 11.4% dalam enam bulan pertama pasca kelulusan menunjukkan perlunya sistem prediksi dini untuk mengidentifikasi faktor-faktor yang mempengaruhi employability mahasiswa. Penelitian ini bertujuan menganalisis dan membandingkan performa tiga algoritma machine learning (Random Forest, Logistic Regression, dan XGBoost) untuk memprediksi status kerja 6 bulan pascawisuda berdasarkan data akademik dan sosial ekonomi. Dataset terdiri dari 3.945 data lulusan dari universitas di Padangsidimpuan dengan variabel program studi, durasi studi, IPK, jenis kelamin, dan penghasilan orang tua. Target operasional adalah status kerja 6 bulan pascawisuda (biner: bekerja=1, belum=0) dengan proporsi kelas bekerja:48.2%, belum:51.8%. Evaluasi menggunakan stratified 5-fold cross-validation dengan metrik akurasi, balanced accuracy, F1-macro, ROC-AUC, dan PR-AUC. Interpretabilitas model dianalisis menggunakan permutation importance dan SHAP values. Random Forest mencapai performa terbaik dengan F1-macro 0.524±0.015, ROC-AUC 0.567±0,012, diikuti Logistic Regression (F1-macro: 0.511±0,018) dan XGBoost (F1-macro: 0.506±0.020). Baseline mayoritas mencapai akurasi 51,8%. Analisis permutation importance mengidentifikasi IPK sebagai faktor paling berpengaruh (importance: 0.082), diikuti penghasilan orang tua (0.067) dan durasi studi (0.041). Model machine learning memberikan peningkatan moderat dibanding baseline mayoritas. IPK dan faktor sosial ekonomi terbukti berpengaruh signifikan terhadap status kerja lulusan. Temuan ini dapat mendukung pengembangan sistem early warning untuk pendampingan mahasiswa berbasis data.

Intellect : Indonesian Journal of Learning and Technological Innovation

Development of an Integrated Raw Material Inventory Management Information System with a Food Menu for Profit and Loss Calculation Using the Rapid Application Development (RAD) Method (Case Study: Bento Kopi Pamulang)

Predicting Employment Status 6 Months After Graduation with Machine Learning Learning : A Comparative Study of 3,945 Indonesian Graduates