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All Journal Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Brahmana : Jurnal Penerapan Kecerdasan Buatan
Batubara, Ela Roza
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Effect Effect of Gradient Descent With Momentum Backpropagation Training Function in Detecting Alphabet Letters Alkhairi, Putrama; Batubara, Ela Roza; Rosnelly, Rika; Wanayaumini, W; Tambunan, Heru Satria
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.v8i1.12183

Abstract

The research uses the Momentum Backpropagation Neural Network method to recognize characters from a letter image. But before that, the letter image will be converted into a binary image. The binary image is then segmented to isolate the characters to be recognized. Finally, the dimension of the segmented image will be reduced using Haar Wavelet. One of the weaknesses of computer systems compared to humans is recognizing character patterns if not using supporting methods. Artificial Neural Network (ANN) is a method or concept that takes the human nervous system. In ANN, there are several methods used to train computers that are made, training is used to increase the accuracy or ability of computers to recognize patterns. One of the ANN algorithms used to train and detect an image is backpropagation. With the Artificial Neural Network (ANN) method, the algorithm can produce a system that can recognize the character pattern of handwritten letters well which can make it easier for humans to recognize patterns from letters that are difficult to read due to various error factors seen by humans. The results of the testing process using the Backpropagation algorithm reached 100% with a total of 90 trained data. The test results of the test data reached 100% of the 90 test data.
Sistem Pendukung Keputusan Penentuan Kenaikan Gaji Karyawan (Kasus PPKS Marihat) Batubara, Ela Roza; Poningsih, P
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 2 (2024): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i2.463

Abstract

Determining employee salary increases is an important aspect of human resource management that influences employee motivation and productivity. This process is often complex and requires accurate evaluation of multiple criteria. This research aims to develop a decision support system (DSS) for determining employee salary increases using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method. The MOORA method was chosen because of its ability to handle various criteria and provide objective results. This system is designed to help managers make faster and more precise decisions by considering various factors such as length of service, attendance and competency. The implementation of this system is expected to increase transparency and accuracy in the process of assessing employee salary increases, as well as reducing the potential for bias and subjectivity. Test results show that this system is effective in providing reliable recommendations for employee salary increase decisions. Thus, this MOORA-based decision support system can be a useful tool for companies in managing human resources more efficiently and fairly.
Quantum Perceptron in Predicting the Number of Visitors to E-Commerce Websites in Indonesian Solikhun, Solikhun; Carissa Arishandy, Dinda; Batubara, Ela Roza; Poningsih
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2334

Abstract

In the current digital era, e-commerce has become the backbone of Indonesia's digital economy, which is experiencing rapid growth. However, competition in this industry is becoming increasingly fierce, indicating the importance of predicting the number of website visitors for an effective marketing strategy. Quantum Perceptron, the latest quantum computing innovation, promises a more accurate and efficient approach compared to conventional methods such as classical Perceptron. This research proposes the use of Quantum Perceptron to predict the number of visitors on large e-commerce platforms in Indonesia. The data used in the research is data on the number of e-commerce visitors obtained from the katadata.com website. Data from Shopee, Tokopedia, Lazada, Blibli, and Bukalapak were used to analyze and compare predictions with classical perceptron methods, showing the significant potential of Quantum Perceptron in supporting the development of more efficient business strategies. The research results show that the Quantum Perceptron algorithm can make predictions very well compared to the classical perceptron, proven by the Quantum Perceptron having a perfect accuracy of 100% with a total of 2 epochs while the classical perceptron has 100% accuracy with a total of 10 epochs. Quantum perceptron has better performance and shorter time, this can be seen from the smaller number of epochs.
PENGENALAN POLA KEMAMPUAN PELANGGAN DALAM MEMBAYAR AIR PDAM MENGGUNAKAN ALGORITMA NAÏVE BAYES Ilmi R.H. Zer, P.P.P.A.N.W. Fikrul; Batubara, Ela Roza; Alkhairi, Putrama; Tambunan, Fazli Nugraha; Rosnelly, Rika
Jurnal TIMES Vol 10 No 2 (2021): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (828.686 KB) | DOI: 10.51351/jtm.10.2.2021656

Abstract

Dengan meningkatnya jumlah MBR (Masyarakat Berpenghasilan Rendah) yang masuk setiap tahunnya dimasing-masing wilayah di Pematansgsiantar, pihak PDAM Tirta Lihou berencana mencari alternatif solusi dalam menangani permasalahan kemampuan pelanggan dalam membayar tagihan air sehingga biaya opersional tetap bisa berjalan baik dan produksi dapat memenuhi kebutuhan masyarakat. Dalam menentukan alternatif untuk menentukan kemampauan masyarakat dalam membayar tagiahan air digunakan metode datamining. Dengan menggunakan teknik datamining khususnya klasifikasi menggunakan algoritma Naive Bayes dapat dilakukan prediksi terhadap kemampauan pelanggan dalam membayar tagihan air bersih berdasarkan data yang ada. Naive bayes adalah teknik prediksi probabilistik sederhana yang berdasarkan pada teorema Bayes dengan asumsi independensi (ketidak tergantungan) yang kuat. Berdasarkan hasil dari perhitungan menggunakan algoritma Naive Bayes, diperoleh hasil klasifikasi dari 30 alternatif yang digunakan, dimana terdapat 11 kelas mampu membayar tagihan dan 19 Tidak Mampu dengan total Accuracy yang diperoleh sebesar 70%. Dari hasil yang diperoleh,diharapkan penelitian ini dapat membantu pihak PDAM Tirta Lihou dalam menentukan lokasi yang layak dilakukan penaybungan sumber air untuk pelanggan yang memiliki prosfek baik dengan kemampuan untuk membayar tagihan air, sehingga dapat meminimalisir kerugian PDAM dan dapat memenuhi kebutuhan masyarakat. Penelitian ini juga diharapkan dapat menjadi referensi bagi peneliti selanjutnya yang berkaitan dengan pengguna algoritma yang digunakan.
Co-Authors Alkhairi, Putrama Carissa Arishandy, Dinda Heru Satria Tambunan, Heru Satria Ilmi R.H. Zer, P.P.P.A.N.W. Fikrul Poningsih Poningsih, P Rika Rosnelly, Rika Solikhun Solikhun, Solikhun Tambunan, Fazli Nugraha Wanayaumini, W