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Application of Scrum Methodology in The Design of Micro, Small, and Medium Enterprise Systems: A Case Study on Laundry Services Basri, Amat; Atmaja, Dewi Marini Umi; Hakim, Arif Rahman; Sanjaya, Andreas Rino
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1968

This study investigates the application of the Scrum methodology in developing systems for Micro, Small, and Medium Enterprises (MSMEs), specifically focusing on laundry service operations. The case study centers on D'Laundry, a laundry service provider, which has traditionally operated with conventional transaction methods. The objective of this research is to develop an MSME system employing the Scrum framework. The system analysis was executed using an Object-Oriented approach, utilizing Unified Modeling Language (UML) for modeling. The development phase employed PHP for application creation, while MySQL was used for the system database. The implementation phase was conducted within a local network, with functional system testing carried out via Black Box testing techniques. Furthermore, the software quality was assessed through a User Acceptance Test, complemented by a questionnaire-based approach. The findings offer insights into the adoption of agile methodologies by MSMEs, emphasizing digital transformation strategies.

Android-Based Herpes Disease Detection Application using Image Processing Hakim, Arif Rahman; Atmaja, Dewi Marini Umi; Tugiman, Tugiman; Basri, Amat
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11913

Herpes is a viral infection that causes a skin disease that is widespread throughout the world. Herpes virus is a DNA virus transmitted via infected skin, saliva, and other body fluids. Herpes is characterized by chickenpox-like nodules in one area of the skin, swollen tissue surrounding the nodule, and blister formation on the nodule. Digital image processing that can detect herpes disease is anticipated to reduce physical contact between physicians and patients during skin disease diagnosis. This study's methodology includes collecting data on herpes disease, developing machine-learning models using the CNN algorithm, and deploying the model as an Android application. This study makes use of actual data collected via smartphones, Pocket Cameras, and internet-sourced photographs. The data include 12,645 images of skin affected by herpes and normal skin. Using 100 epochs and the Adadelta optimizer, the accuracy of this study is 85 percent.

Implementasi Sistem Pakar untuk Diagnosis Penyakit Lambung Menggunakan Pendekatan Fuzzy Mamdani Berbasis Website Yansyah, Ilham Roni; Atmaja, Dewi Marini Umi; Hakim, Arif Rahman; Suwaryo, Niko
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3534

This study aims to develop a more specific diagnostic approach for various gastric diseases in humans, such as gastritis, peptic ulcers, gastric cancer, gastric tumors or polyps, dyspepsia, gastroesophageal reflux disease (GERD), gastroparesis, and gastroenteritis. This approach seeks to enhance the accuracy of disease identification based on more detailed symptoms. An expert system utilizing the Fuzzy Mamdani method is designed to reduce reliance on internal medicine specialists, enabling patients to gain preliminary insights into the type of gastric disease they may have. This expert system is implemented on a web-based platform, leveraging information technology to integrate large-scale databases, supporting efficiency, accuracy, and relevance to the latest developments in medical science. By analyzing digestive disorder symptoms, the system can provide detailed diagnoses, offer insights into identified symptoms, and recommend appropriate treatment solutions.

Implementasi Sistem Pakar untuk Diagnosis Penyakit Lambung Menggunakan Pendekatan Fuzzy Mamdani Berbasis Website Yansyah, Ilham Roni; Atmaja, Dewi Marini Umi; Hakim, Arif Rahman; Suwaryo, Niko
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3534

This study aims to develop a more specific diagnostic approach for various gastric diseases in humans, such as gastritis, peptic ulcers, gastric cancer, gastric tumors or polyps, dyspepsia, gastroesophageal reflux disease (GERD), gastroparesis, and gastroenteritis. This approach seeks to enhance the accuracy of disease identification based on more detailed symptoms. An expert system utilizing the Fuzzy Mamdani method is designed to reduce reliance on internal medicine specialists, enabling patients to gain preliminary insights into the type of gastric disease they may have. This expert system is implemented on a web-based platform, leveraging information technology to integrate large-scale databases, supporting efficiency, accuracy, and relevance to the latest developments in medical science. By analyzing digestive disorder symptoms, the system can provide detailed diagnoses, offer insights into identified symptoms, and recommend appropriate treatment solutions.

Implementation of Random Forest Algorithm on Palm Oil Price Data Rahman Hakim, Arif; Atmaja, Dewi Marini Umi; Basri, Amat; Syafii, Muhamad
Tech-E Vol. 6 No. 2 (2023): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v6i2.1757

One of the potential commodities that are widely cultivated in Indonesia is palm oil, and palm oil or commonly referred to as palm oil is one of the processed products of palm oil which generates the most important foreign exchange for Indonesia. Data mining is a process that utilizes mathematical techniques, statistics, artificial intelligence, and machine learning techniques to extract and identify useful information and related knowledge from large databases [3], including palm oil price data. Random Forest is one of the methods in the decision tree. A decision tree is a flowchart shaped like a tree with a root node that is used to collect data that is used to solve problems and make decisions. In this study, a random forest algorithm was used to classify palm oil price data from 2014 to 2019. The classification method used the random forest algorithm on palm oil data using the Mtry parameter of 1 and the Ntree parameter of 500 resulting in an accuracy percentage of 100%. The most influential variable (importance variable) in the classification model using the resulting random forest algorithm is the palm oil variable.

Analisis dan Perancangan Sistem Elektronik Toko (E-Toko) Kelontong Berbasis Website Tugiman, Tugiman; Atmaja, Dewi Marini Umi; Suwaryo, Niko
Innovative: Journal Of Social Science Research Vol. 4 No. 2 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i2.9505

Perkembangan teknologi informasi saat ini sangat berkembang pesat dan menjangkau ke semua bidang usaha, organisasi, perusahaan, dan sebagainya. Berdasarkan data yang dikeluarkan oleh BEKRAF (2018), skala usaha besar 3,12%, menengah 11,90%, kecil 32,01%, dan mikro sebanyak 52,97%. Usaha Mikro Kecil Menengah (UMKM) di Indonesia, khususnya pada sektor usaha warung dan toko kelontong berkembang sangat pesat. Terkait hal ini peneliti mengadakan penelitian dengan membuat sistem eletronik toko kelontong (e-toko) berbasis web. Pengujian aplikasi ini menggunakan UserAcceptance Test (UAT). Metode ini dipilih untuk mengetahui mengenai penerimaan sistem dan kebergunaanya. Berdasarkan pengujian black box semua hasil pengujian menunjukkan valid, sedangkan pada pengujian penerimaan sistem menggunakan User Acceptance Test (UAT) menghasilkan angka 80.02% dengan kriteria baik.

Implementation of Support Vector Regression for Polkadot Cryptocurrency Price Prediction Haryadi, Deny; Hakim, Arif Rahman; Atmaja, Dewi Marini Umi; Yutia, Syifa Nurgaida
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.945

Cryptocurrency investment is an investment instrument that has high risk but also has a greater advantage than other investment instruments. To make a big profit, investors need to analyze cryptocurrency investments to predict the price of the cryptocurrency to be purchased. The highly volatile movement of cryptocurrency prices makes it difficult for investors to predict those prices. Data mining is the process of extracting large amounts of information from data by collecting, using data, the history of data relationship patterns, and relationships in large data sets. Support Vector Regression has the advantage of doing accurate cryptocurrency price predictions and can overcome the problem of overfitting by itself. Polkadot is one of the cryptocurrencies that are often used as investment instruments in the world of cryptocurrencies. Polkadot cryptocurrency price prediction analysis using the Support Vector Regression algorithm has a good predictive accuracy value, including for Polkadot daily closing price data, namely with a radial basis function (RBF) kernel with cost parameters C = 1000 and gamma = 0.001 obtained model accuracy of 90.00% and MAPE of 5.28 while for linear kernels with parameters C = 10 obtained an accuracy of 87.68% with a MAPE value of 6.10. It can be concluded that through parameter tuning, the model formed has an accuracy value and the best MAPE is to use a radial kernel basis function (RBF) with cost parameters C = 1000 and gamma = 0.001. The results show that the Support Vector Regression method is quite good if used for the prediction of Polkadot cryptocurrencies.

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