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Perancangan Sistem Informasi Inventaris pada PT. Rejoso Manis Indo Menggunakan Metode Rapid Application Development Panky Yoga Pratama; Abd. Charis Fauzan; Tito Prabowo
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 01 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i01.1209

Abstract

Technology that cannot be stopped and is increasingly developing requires companies like PT. Rejoso Manis Indo to adopt a more sophisticated and efficient system. This company's still manual inventory management process faces various problems, such as recording errors, lost or damaged data, and low efficiency. Difficulty tracking the status of goods also results in inaccurate data. This research aims to design an inventory information system using the Rapid Application Development (RAD) method which involves users at every stage of development. Data was collected through Likert scale questionnaires, interviews, and literature studies. This system was implemented using PHP CodeIgniter, and MySQL, and checked using the System Usability Scale (SUS) and black box testing. The research results show that the developed inventory information system increases effectiveness and efficiency, with a user satisfaction level of 77 with a value of B (Good). In conclusion, this system is effective in overcoming inventory problems at PT. Rejoso Manis Indo, although further research is needed to involve more users from various departments and examine the security and scalability aspects of the system.
Penerapan Metode Weighted Product Berbasis Visualisasi Graph Database dalam Merekomendasikan Parfum Isi Ulang Defy Lukbatul Qolbiah; Abd. Charis Fauzan; Tito Prabowo
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6181

Abstract

Perfume is useful for increasing self-confidence, creating satisfaction, eliminating bad odors, and making self-assessment more attractive. Refill perfumes are made from certain perfume seeds dissolved in a suitable solvent. Perfume has many types and strengths of aroma, but there are obstacles when people want to choose the desired perfume scent. This problem becomes research material because it is expected that this problem can be solved. To determine perfume recommendations, it is calculated using the Weighted Product method and visualized using a graph database. In the Neo4j Graph Database visualization, the perfume category and perfume name are used as nodes and the ranking results are used as edges. From the ranking results using the Weighted Product method, 21 perfumes for each category are entered into the Graph Database visualization and a total of 63 perfumes will appear in the perfume recommendation system.Refill perfume is a perfume made from certain perfume seeds dissolved in the appropriate solvent.
Analisis Deret Waktu untuk Forecasting Populasi Ternak di Indonesia dengan Model LSTM Tito Prabowo; Lestariningsih; Abd. Charis Fauzan; Veradella Yuelisa Mafula
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7566

Abstract

Livestock population in Indonesia is one of the key indicators supporting national food security, particularly in meeting the demand for animal-based protein. However, the suboptimal utilization of livestock population data for strategic planning remains a challenge in the livestock sector. This study aims to predict livestock population in Indonesia using the Long Short-Term Memory (LSTM) method, a variant of Recurrent Neural Network (RNN) designed for time series data analysis. The livestock population data used in this research was obtained from the Central Statistics Agency (BPS) for the period of 2006 to 2022. The LSTM model was trained using 80% of the data for training and 20% for testing, with evaluation conducted using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the LSTM model can forecast the national livestock population up to 2033 with good accuracy, particularly for livestock such as goats (MAPE 5.47%) and beef cattle (MAPE 5.64%). However, a higher error rate was observed for buffalo (MAPE 16.57%). The predictions indicate a significant growth trend in poultry populations, such as broiler chickens and laying hens. In conclusion, this model can support data-driven decision-making to ensure stable and sustainable animal protein availability, thereby strengthening national food security.