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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Semantik JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Inspiration Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IJISTECH (International Journal Of Information System & Technology) Indonesian Journal of Electrical Engineering and Computer Science Randwick International of Social Science Journal IJISTECH Journal La Multiapp

Elviawaty Muisa Zamzami

Faculty Of Computer Science And Information Technology, University Of North Sumatra

Author-ID : 427598

Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Social Sciences Other

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SEBUAH KAJIAN TENTANG REQUIREMENTS RECOVERY PADA AREA RISET REVERSE ENGINEERING Elviawaty Muisa Zamzami; Eko Kuswardono Budiardjo
Jurnal Sistem Informasi Vol 5, No 2 (2013)
Publisher : Universitas Sriwijaya

Ketiadaan dokumen requirements ataupun ketidaksesuaian isi dokumen requirements dengan peranti lunak jadi (existing software) dapat menjadi permasalahan dalam pengembangan atau pemeliharaan peranti lunak. Tanpa diketahui hal-hal yang menjadi requirements dapat menyebabkan peranti lunak sebagai produk yang gagal karena tidak memenuhi keinginan kustomer. Karenanya, diperlukan usaha untuk memperoleh kembali requirements (requirements recovery). Requirements recovery termasuk kedalam area riset reverse engineering. Meskipun requirements termasuk sebagai kunci sukses pengembangan peranti lunak, namun keberadaan riset requirements recovery relatif minim dibandingkan dengan riset reverse engineering lainnya. Paper ini membahas tentang requirements recovery yang berada pada area riset reverse engineering, termasuk beberapa riset requirements recovery yang ada. Juga, memuat requirements recovery dari peranti lunak jadi dapat dilakukan dengan mengenali end-to-end interaction antara user dengan peranti lunak.Kata Kunci: requirements, requirements recovery, reverse engineering, end-to-end interaction

Implementation of ANN with the Cyclical Order Method For Forecasting the Life Span of the World’s Population Muhammad Rizal; Elviawaty Muisa Zamzami
IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

This study aims to predict the age (life expectancy) of the world's population. This research is the development of research that has been done before. But in this study only to get the best architectural model to predict the age (life expectancy) of the world's population, using the Cyclical Order method. Whereas in this follow-up research, it will produce forecasting in the form of age (life expectancy) of the population in the world based on a model that has been obtained from previous research. The research data is the age data (life expectancy) of the world's population from the United Nations: "World Population Prospect: The 2010 Revision Population Database". This study uses 5 architectural models including: 3-5-1, 3-8-1, 3-10-1, 3-5-8-1 and 3-5-10-1. Of the 5 models used, architectural models 3-5-10-1 are the best with an accuracy of 97%, the value of MSE training is 0,0009979400 and MSE testing is 0,0008358919. Forecasting results from this study are expected to be a reference for governments in the world, especially Indonesia to pay more attention to the level of health and well-being of its population so that the level of life of the population is getting better and higher.

Utilization of Neural Network for Local Processing on the Internet of Things (IoT) Boy Yako Siahaan; Elviawaty Muisa Zamzami; Suherman Suherman
Randwick International of Social Science Journal Vol. 3 No. 3 (2022): RISS Journal, July
Publisher : RIRAI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47175/rissj.v3i3.516

This research is intended to produce a smart sensor system using a Neural Network which is implemented through the design of an Occupation monitoring tool and application (presence). Sensor equipment uses Arduino UNO, EST8266 Wifi, DHT22 and LDR. The readings of these sensors are then sent remotely via the internet and read by the application. Based on the research training conducted, a prediction model will be born where this model will translate the sensor data received and then generate data on the presence in an environment where the sensor is placed. IoT sensor tools and applications that are placed in several locations are very useful in carrying out daily human activities such as monitoring security in the home environment, schools, offices and warehouses for storing goods.

Reduksi Atribut Menggunakan Chi Square untuk Optimasi Kinerja Metode Decision Tree C4.5 Anirma Kandida Br Ginting; Maya Silvi Lydia; Elviawaty Muisa Zamzami
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 1 (2023): Volume 9 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i1.56542

Pada metode decision tree C4.5, proses split atribut masih belum dapat secara maksimal mengoptimalkan kinerja akurasi pada decision tree yang disebabkan oleh noisy pada atribut yang kurang relevan. Hal tersebut berimplikasi terhadap ukuran dari pohon keputusan menjadi over-fitting sehingga perolehan akurasi pengujian menjadi kurang maksimal. Reduksi atribut merupakan salah satu cara yang dapat dilakukan dalam melakukan seleksi terhadap atribut data yang memiliki persentase pengaruh cenderung kecil sehingga diharapkan mampu dalam meningkatkan akurasi pada metode klasifikasi data. Adapun metode yang diusulkan pada penelitian ini yang digunakan untuk mereduksi atribut yang kurang relevan dari dataset yaitu dengan metode Chi Square sehingga menghasilkan atribut yang mempunyai pengaruh besar terhadap data dan kemudian diklasifikasikan menggunakan decision tree C4.5. Untuk melakukan pengujian terhadap model yang diusulkan, maka penelitian ini menggunakan dataset dari kaggle.com yaitu South Germany Credit yang terdiri dari 1000 records data dengan 20 atribut. Evaluasi kinerja klasikasi yang diusulkan yaitu berdasarkan Confusion Matrix. Dari hasil uji metode yang diusulkan, didapatkan kesimpulan bahwa metode yang diusulkan mampu meningkatkan akurasi decision tree c4.5 dengan rata-rata peningkatan akurasi sebesar 2.5%.

MODEL PREDIKSI PROFIL PELANGGAN BERDASARKAN KLASIFIKASI MELALUI PENDEKATAN SUPPORT VECTOR MACHINE Kiro, Ogiana; Mawengkang , Herman; Zamzami, Elviawaty Muisa
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11608

Nowadays the market is characterized globally, products and services are almost identical and there are many suppliers. The most important aspect in classifying data in data mining is classification. Classification techniques have been widely used in many problems in research. The purpose of this research is to build a model that can predict behavior based on the information of each customer. This research was conducted by making a Prediction Model of Customer Profile Based on Classification Through the Support Vector Machine Approach which aims to obtain a package prediction accuracy value that is suitable for WO (Wedding Organizer) customers in classifying based on the profile of prospective customers. In the optimization results on the SVM model kernel function, the linear and polynomial kernels get the same accuracy value on the training data of 99.29% and the testing data of 94.92%. The lowest accuracy value was obtained in the RBF kernel function of 97.16% on training data and 96.61% on testing data. the best precision class value in the data testing was obtained in the basic package at 100%. The total value of the appropriate prediction on the training data was obtained by 56 samples from a total of 59 samples, and 3 samples that did not match the prediction with an accuracy of 94.92% on the data testing

Convolutional Neural Network Activation Function Performance on Image Recognition of The Batak Script Muis, Abdul; Zamzami, Elviawaty Muisa; Erna Budhiarti Nababan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13192

Deep Learning is a sub-set of Machine learning, Deep Learning is widely used to solve problems in various fields. One of the popular deep learning architectures is The Convolutional Neural Network (CNN), CNN has a layer that transforms feature extraction automatically so it is widely used in image recognition. However, CNN's performance using the tanh function is still relatively low, therefore it is necessary to select the right activation function to improve accuracy performance. This study analyzes the use of the activation function in image recognition of the Batak script. The result of this study is that the CNN model using the ReLU and eLU functions produces the highest accuracy compared to the CNN model using the tanh function. The CNN model using eLU produces the best accuracy performance in the training process, which is 99.71% with an error value of 0.0108. Meanwhile, in the testing process, the highest accuracy value is generated by the CNN Model using the ReLU function with an accuracy of 94.11%, an error value of 0.3282, a precision value of 0.9411, a recall of 0.9411, and an f1-score of 0.9416.

Customer Segmentation of Mobile Banking Users Using Feature Engineering and K-Means Clustering Ania, Hijja; Mahyuddin, Mahyuddin; Zamzami, Elviawaty Muisa
Journal La Multiapp Vol. 6 No. 3 (2025): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v6i3.2377

The increasing adoption of mobile banking has necessitated deeper insights into user behavior to enable banks to design personalized and targeted marketing strategies. This study aims to segment mobile banking customers based on their transaction patterns, specifically in the purchase of prepaid mobile credit and internet packages, using feature engineering techniques and the K-Means clustering algorithm. A dataset comprising over one million transactions from a regional bank in North Sumatra, Indonesia, was analyzed. Behavioral and time-based features were extracted to capture customer activity levels, transaction values, temporal preferences, and product usage. The Elbow Method identified five optimal clusters, each representing unique user profiles, including occasional users, regular low-value users, premium users, heavy users, and moderate-consistent users. Findings indicate strong operator loyalty and consistent transaction timing across segments, especially in early-month activity. The results offer practical implications for financial institutions seeking to enhance customer engagement, retention, and service personalization through behavior-based segmentation strategies. This study also contributes methodologically by showcasing the utility of unsupervised machine learning in deriving customer insights from transactional data without relying on sensitive demographic information.

Co-Authors Abdul Muis Ania, Hijja Anirma Kandida Br Ginting Boy Yako Siahaan Eko Kuswardono Budiardjo Eko Kuswardono Budiardjo Erna Budhiarti Nababan Hayanti, Septi Indah Mulyani Kiro, Ogiana Lubis, Arif Ridho Mahyuddin K. M Nasution Mahyuddin Mahyuddin Mawengkang , Herman Maya Silvi Lydia Muhammad Rizal Muhammad Zarlis Niskarto Zendrato Opim Salim Sitompul Suherman Suherman

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