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All Journal Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB SAINTEK
Hemdani Rahendra Herlianto
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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Sistem Informasi Point of Sales (POS) Pada CV Sedulur Teknik dengan Teknik Menggunakan Metode Agile Hemdani Rahendra Herlianto; Muhammad Chaidir Alam
Prosiding Sains dan Teknologi Vol. 2 No. 1 (2023): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 2 - Februari 2023
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Processing of goods and sales data on CV Sedulur Teknik still uses manual methods, to find out the goods sold and sales transactions must be calculated by means of a bookkeeping system. The calculation of sales turnover must be calculated using a calculation tool such as a calculator because there is no sales system so that some incoming sales data does not match which has an impact on the difference in nominal sales figures and this manual method makes work longer. So a point of sales system was created on CV Sedulur Teknik in order to assist in the transaction process. This sales system uses PHP (Hypertext Preprocessor) as the programming language used, and Mysql as the database used and Xampp to connect the database to the website. The method used is the agile scrum pattern method, scrum itself is a software that is used agile to stand in a team, additional product processes and software development that focuses on speed and is carried out by realizing the final result.
Implementasi Algoritma Naïve Bayes Classifier Sinkronisasi Absensi Dengan Prestasi Akademik Pasraman Taman Dharma Widya Hemdani Rahendra Herlianto; I Gede Krishna Yogananda Raken Putra
Prosiding Sains dan Teknologi Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Data mining is a technique that utilizes large amounts of data to obtain valuable information that was previously unknown and can be used to support important decision-making. In this study, the author attempts to mine customer data from an insurance company to determine whether the customers are categorized as current, less current, or delinquent in their payments. The existing data are analyzed using the Naive Bayes algorithm. Naive Bayes is one of the methods in probabilistic reasoning. The Naive Bayes algorithm aims to classify data into specific classes; the resulting patterns can then be used to predict students’ academic performance based on attendance records, enabling Pasraman Taman Dharma Widya to make appropriate decisions regarding the students.
Analisa Raw Material Untuk Produksi Seng Pasir Merah Dengan Algoritma Regresi Linier (Studi Kasus: Pt. Harapan Sukses Jaya) Hemdani Rahendra Herlianto; Muhammad Amirrullah
Prosiding Sains dan Teknologi Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Zinc is a commonly used roofing material in Indonesia, alongside roof tiles and similar products. In the production of red sand zinc, two types of raw materials are used: **base coat** (base layer) and **top coat** (top layer). This study aims to analyze and process data on excess raw material usage in the production line, warehouse requests, and production reports to predict raw material needs before production begins. The study applies the **Linear Regression Algorithm** in data mining, as it can generate predictions according to existing patterns and integrate raw material usage with production reports. Testing with RapidMiner produced an RMSE of 0.500, categorized as good. The accuracy, calculated using the MAPE formula, was 0.50%, equivalent to 99.5% accuracy. These results indicate that Linear Regression is effective for predicting raw material usage quickly, accurately, and reliably, enabling companies to plan material requirements efficiently and improve production management.
Kepuasan Pelanggan Dalam Rancangan Sistem Pelayanan Surat Pengantar di Kelurahan Mustika Jaya Bekasi Hemdani Rahendra Herlianto; Muhammad Richi
Jurnal SIGMA Vol 14 No 4 (2023): Desember 2023
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v14i4.7318

Abstract

Penelitian ini membahas implementasi sistem informasi pelayanan administrasi surat di Kelurahan Mustika Jaya, Kota Bekasi, yang selama ini masih dilakukan secara manual sehingga menyebabkan keterbatasan waktu pelayanan, kurangnya informasi persyaratan, proses pembuatan surat yang lama, serta potensi kesalahan pengetikan. Untuk mengatasi permasalahan tersebut, dikembangkan sistem informasi berbasis web yang bertujuan mempermudah masyarakat dalam mengurus surat pengantar dan keterangan secara lebih cepat dan efisien. Pengumpulan data dilakukan melalui studi pustaka, observasi, dan wawancara. Sistem dikembangkan menggunakan pendekatan Object Oriented Programming dengan pemodelan Unified Modeling Language (UML), bahasa pemrograman PHP, serta basis data MySQL, dan diuji menggunakan metode Black Box Testing. Hasil penelitian menunjukkan bahwa sistem informasi pelayanan surat berbasis web mampu meningkatkan efektivitas dan efisiensi proses administrasi serta memudahkan petugas kelurahan dalam memberikan pelayanan kepada masyarakat.
Analisis Sentimen Terhadap Ulasan Pengguna Gopay: Studi Perbandingan Algoritma Support Vector Machine Dan Naive Bayes Hemdani Rahendra Herlianto; Bhagas Shaib Pramono
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i2.7324

Abstract

Penelitian ini bertujuan menganalisis sentimen ulasan pengguna GoPay serta membandingkan kinerja algoritma Naïve Bayes dan Support Vector Machine (SVM). Data sebanyak 500 ulasan berbahasa Indonesia dikumpulkan secara otomatis dari Google Play Store menggunakan google-play-scraper. Data melalui tahapan pra-pemrosesan meliputi pembersihan teks, case folding, stopword removal, stemming, dan translasi ke bahasa Inggris. Pelabelan sentimen dilakukan menggunakan TextBlob dan menghasilkan tiga kelas: positif, netral, dan negatif. Untuk mengatasi ketidakseimbangan kelas, dilakukan augmentasi pada data minoritas. Fitur diekstraksi menggunakan TF-IDF dan data dibagi menjadi 80% data latih dan 20% data uji. Hasil pengujian menunjukkan bahwa SVM memberikan performa terbaik dengan akurasi 0,94, lebih tinggi dibandingkan Naïve Bayes sebesar 0,88. Selain itu, SVM juga menunjukkan nilai precision, recall, dan F1-score yang lebih konsisten. Dengan demikian, SVM lebih efektif dalam mengklasifikasikan sentimen ulasan pengguna GoPay.
Co-Authors Bhagas Shaib Pramono I Gede Krishna Yogananda Raken Putra Muhammad Amirrullah Muhammad Chaidir Alam Muhammad Richi