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All Journal Jurnal Matematika JURNAL SISTEM INFORMASI BISNIS Jupiter TELKOMNIKA (Telecommunication Computing Electronics and Control) GRADIEN Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah Ekonomi Islam JPM (Jurnal Pemberdayaan Masyarakat) BAREKENG: Jurnal Ilmu Matematika dan Terapan Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal AKSI (Akuntansi dan Sistem Informasi) Martabe : Jurnal Pengabdian Kepada Masyarakat Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Informatika Global Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal Abdimas Mahakam DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Jurnal Abdimas Mandiri Abdimas Galuh : Jurnal Pengabdian Kepada Masyarakat Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Generic Jurnal Nasional Ilmu Komputer Jurnal Pengabdian dan Peningkatan Mutu Masyarakat (JANAYU) Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Jurnal Penelitian Pendidikan Matematika JUSTIN (Jurnal Sistem dan Teknologi Informasi) Accounting and Finance Studies Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Akuntansi INTECH (Informatika dan Teknologi)

Imelda Saluza, Imelda

Universitas Indo Global Mandiri

Author-ID : 1254373

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Optimalisasi Klasifikasi Kanker Payudara Menggunakan Forward Selection pada Naive Bayes Lastri Widya Astuti; Imelda Saluza; Faradilla Faradilla; M. Fadhiel Alie
Jurnal Informatika Global Vol 11, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v11i2.1235

Breast cancer is a type of malignant tumor which is still the number one killer where the process of spread or metastasis takes a long time. The number of breast cancer sufferers increases every year so that if detected or caught early, prevention can be done early so as to reduce the number of breast cancer sufferers. To reduce the risk of increasing the number of cancer patients, it is necessary to do early detection, several methods can be used to assist the early detection process such as cancer screening, or computational methods. Several machine learning methods that have been chosen to solve cases of breast cancer prediction, especially the classification algorithm, including Naive Bayes have the advantage of being simple but having high accuracy even though they use little data. Weaknesses in Naive Bayes, namely the prediction of the probability result is not running optimally and the lack of selection of relevant features to the classification so that the accuracy is low. This research is intended to build a classification system for detecting breast cancer using the Naive Bayes method, by adding a forward selection method for feature selection from the many features that exist in breast cancer data, because not all features are features that can be used in the classification process. The result of combining the Naive Bayes method and the forward selection method in feature selection can increase the accuracy value of 96.49% detection of breast cancer patients.

Feature Selection Menggunakan Binary Wheal Optimizaton Algorithm (BWOA) pada Klasifikasi Penyakit Diabetes Lastri Widya Astuti; Imelda Saluza; Evi Yulianti; Dhamayanti Dhamayanti
Jurnal Informatika Global Vol 13, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i1.2057

Diabetes Mellitus (DM) is a chronic disease characterized by blood glucose (blood sugar) levels exceeding normal, i.e. blood sugar levels being equal to or more than 200 mg/dl, and fasting blood sugar levels being above or equal to 126 mg/dl. The increase in the number of people with diabetes is due to delays in detection. Utilization of machine learning in helping to establish a fast and accurate diagnosis is one of the efforts made in the health sector. One of the important steps to produce high classification accuracy is through the selection of relevant features. The problem in feature selection is dimensionality reduction, where initially all attributes are required to obtain maximum accuracy while not all features are used in the classification process. This study uses the Binary wheal Optimization Algorithm (BWOA) as a feature selection method to increase accuracy in the classification of diabetes mellitus. The use of metaheuristic algorithms is an alternative to increase computational efficiency and avoid local minimums. The BWOA algorithm reduces the 8 attributes in the dataset to the 3 best attributes that are able to represent the original dataset. The results showed that from the six classification methods tested, namely: K-NN, Naïve Bayes, Random Forest, Logistics Regression, Decision Tree, Neural Network. then the three logistic regression methods, naive Bayes and neural network are in good classification criteria based on Area Under Curve (AUC) while the calculation of the accuracy value shows an average of above 70%. Keywords : Feature Selection, Classification, Diabetes Mellitus, Accuracy, Area Under Curve (AUC)

Prediksi Data Time Series Harga Penutupan Saham Menggunakan Model Box Jenkins ARIMA Imelda Saluza; Dewi Sartika; Lastri Widya Astuti; Faradillah Faradillah; Leriza Desitama; Endah Dewi Purnamasari
Jurnal Informatika Global Vol 12, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v12i2.1940

The ability to predict time series data on closing market prices is critical in determining a company's stock results. The development of an efficient stock market has a positive correlation with economic growth, in a country both in the short and long term. In practice, investors tend to invest in countries that have a stable economy, low crime. The rise and fall of stock prices has made many investors develop various effective strategies in predicting stock prices in the future with the aim of making investment decisions so that investors can guarantee their profits and minimize risk.As a result, the researchers developed a model that could accurately estimate precision. Time series data models are one of the most powerful methods to render assumptions in decisions containing uncertainty. The AutoRegressive Integrated Moving Average (ARIMA) model with the Box Jenskins time series procedure is one of the most commonly used prediction models for time series results. The steps for using the Box Jenskins ARIMA model for historical details of expected stock closing prices are outlined in this paper. BBYB and YELO stock data from yahoo.finance were used as historical data. The Aikake Information Criterion (AIC), Bayesian Information Criterion (BIC) / Schawrz Bayesia Criterion (SBC), Log Probability, and Root Mean Square Error (RMSE) are used to choose an effective model, and the model chosen is ARIMA (1 , 1,2). The findings suggest that the Jenkins ARIMA box model has a lot of scope for short-term forecasting, which may help investors make better decisions. Keywords: prediction, the stock's current closing price, Box Jenskins ARIMA model

Optimisasi Backpropagation Neural Network dalam Memprediksi IHSG Hartati Hartati; Alpin Herman Saputra; Imelda Saluza
Jurnal Informatika Global Vol 13, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i1.2066

Covid-19 has become a global epidemic and has spread to many countries in the world, including Indonesia. The COVID-19 pandemic is one source of uncertainty that causes financial data to fluctuate and cause data to be volatile. This outbreak had an impact on financial data, not only on the Rupiah exchange rate but also on the Jakarta Composite Index (JCI). The uncertainty of the JCI makes it difficult for investors, data managers, and business people to predict data for the future. JCI is one indicator of the capital market (stock exchange). The uncertainty of the JCI data causes the need for predictions, so that investors, data managers, and business people can make the right decisions so that they can reduce risk and optimize profits when investing. One of the factors causing the JCI's decline was sentiment caused by investor panic over the rapid spread of COVID-19 in various cities in Indonesia. This research uses Backpropagation Neural Network (BPNN) in making predictions and continues with optimization of BPNN using ensemble techniques. Historical data from the JCI used were obtained from yahoo.finance. The ensemble technique used consists of two approaches, namely combining different architectures and initial weights with the same data and combining different architectures and weights. The results of network performance using ensemble technique optimization show good performance and can outperform the individual network performance of BPNN. Keywords: prediction, JCI, Optimization, BPNN, volatile

Analisis Evaluasi Keberlanjutan E-Filling di Kota Palembang Dewi Sartika; Imelda Saluza
Jurnal Informatika Global Vol 9, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

AbstractDJP continues to optimize the collection of tax returns by facilitating a technology-based tax service system, one of which is e-filing that has been running since 2016. However, e-filing turned out to have less influence on the delivery of Tax Returns (SPT) as reflected in the electronic SPT monitoring data that only met 78% of the 2017 target. This is caused by various problems that arise during the use of e-filing such as individual technology capabilities, loss of efin, forgetting DJP Online account passwords to lack of awareness about the importance of submitting SPT. Problems encounter during the use of e-filing are the basis for evaluating the continued use of e-filling in Palembang. The development of a conceptual model was conducted to evaluate the sustainability of the use of e-filing. The development of a conceptual model basically has a scarcity of supporting theories used and has a complex model. To overcome this problem, Partial Least Squares (PLS) Structural Equation Model (SEM) could be applied to. The results of data analysis found that information quality and service quality did not have a positive influence on the sustainability of the use of e-filing and the level of correlation between information quality, system quality, service quality, and individual ability was still small towards the sustainability of the use of e-filing. The findings of this research are very important for the KPP Pratama in Palembang to analyze the sustainability of the use of e-filing that has been proven empirically, multidimensional and in a specific context. This knowledge could be used as a reference to improve overall quality of taxation for the sake of sustainable use of e-filing.Keywords : SPT, e-filing, PLS SEMAbstrakDJP terus berupaya mengoptimalkan pengumpulan Surat Pemberitahuan Tahunan (SPT) pajak dengan memfasilitasi sistem pelayanan perpajakan berbasis teknologi, salah satunya adalah e-filing yang telah berjalan sejak tahun 2016. Namun, e-filing ternyata kurang berpengaruh terhadap penyampaian SPT yang tergambar pada data hasil monitoring SPT elektronik yang hanya memenuhi 78% dari target sasaran tahun 2017. Hal ini disebabkan oleh berbagai masalah yang muncul selama pemanfaatan e-filing seperti kemampuan teknologi individu, kehilangan efin, lupa password akun DJP Online hingga kurangnya kesadaran tentang pentingnya penyampaian SPT. Permasalahan selama penggunaan e-filing menjadi dasar untuk melakukan evaluasi terhadap keberlangsungan penggunaan e-filling di Palembang. Pengembangan model konseptual dilakukan untuk mengevaluasi keberlanjutan penggunaan e-filing. Pengembangan model konseptual pada dasarnya memiliki kelangkaan teori-teori pendukung yang digunakan dan memiliki model yang kompleks. Untuk mengatasi masalah ini dapat menggunakan Partial Least Squares (PLS) Structural Equation Model (SEM). Hasil analisis data mendapatkan temuan bahwa kualitas informasi dan kualitas layanan tidak memiliki pengaruh positif terhadap keberlanjutan penggunaan e-filing dan tingkat korelasi antara kualitas informasi, kualitas system, kualitas layanan, dan kemampuan individual masih kecil terhadap keberlanjutan penggunaan e-filing. Temuan peneliti ini sangat penting bagi pihak KPP Pratama kota Palembang untuk menganalisa keberlanjutan penggunaan e-filing yang telah dibuktikan secara empiris, multidimensional dan konteks yang spesifik. Pengetahuan ini dapat dapat menjadi acuan untuk meningkatkan kualitas secara keseluruhan demi keberlanjutan penggunaan e-filing.Kata kunci : SPT, e-filing, PLS SEM,

Integrasi Model Kesuksesan Adopsi E-Commerce Berbasis Technological Frames of References (TFR) – Knowledge Management (KM) Faradillah Faradillah; Imelda Saluza; Muhammad Fadhiel Alie; Andini Utari Putri
Jurnal Informatika Global Vol 13, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i2.2306

This study aims to examine several variables supporting the implementation of e-Commerce websites as digital platforms in supporting the business activities of SMEs. Several previous studies have found several variables/factors that support the implementation of e-Commerce with each perspective raised, so that in this study the variables/factors raised are the extraction of all factors from various perspectives and are expected to accommodate different points of view and previous research. Factor extraction is carried out using the Principal Component Analysis (PCA) method using the SPSS 22 Version tool to identify variables/factors from various and various needs, several factors that have components will be extracted and viewed in one category so as to minimize ambiguity factors as e-Commerce Enablers. In this study, data were collected through interviews and online dissemination to Clothing Line Business Actors and staff using digital platform users as many as 75 respondents and obtained some supporting data related to the use of digital platforms in the application of e-Commerce in daily business. Based on the extraction results obtained 5 main drivers that become e-Commerce Enabler based on several models of e-Commerce implementation based on Technological Frames of References (TFR) and Knowledge Management, namely: Organizational Trigger (OT), Environmental Trigger (ET), Individual Intention (II) , Knowledge Aspect and Capability (KAC), and Technology Infrastructure (IT) with each factor with components extracted so as to minimize ambiguity and still accommodate various needs and perspectives.Keywords : Specification Design, Information System, Integrated Document, Rational Unified Process, MASS Cargo

Perbandingan Akurasi Metode Principal Component Analysis (PCA) dan Correlation-Based Feature Selection (CFS) Pada Klasifikasi Perpanjangan Kontrak Karyawan Menggunakan Metode Naïve Bayes Dewi Sartika; Imelda Saluza; Muhammad Haviz Irfani
Jurnal Informatika Global Vol 13, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i2.2292

PT. Oasis Waters International Palembang conducts regular staff performance reviews, the findings of which are utilized to make recommendations for employee contract extension. The Human Resource Department has assigned a numerical value to 25 qualities (HRD). The process of giving a label or class to a number of examples when the value of each characteristic is known as classification. The Naïve Bayes technique is a basic classification approach that makes use of probability estimates. Based on the observations, it was discovered that one of the 25 criteria was deemed the most relevant in determining the recommendation for an employee contract renewal. As a result, in this study, a comparison of the pre-processing Principal Component Analysis (PCA) approach and the Correlation-based Feature Selection (CFS) method on the categorization of employee contract extensions at PT Oasis Waters International Palembang will be performed. According to the data, the CFS approach has a positive influence on classification performance, while PCA does not. This is demonstrated by a 30% increase in accuracy when utilizing the CFS approach. Meanwhile, both strategies have a positive influence on the model's dependability. This is demonstrated by a reduction in Root Mean Square Error (RMSE) when using the CFS approach from 0.6325 to 0.1845, whereas using the PCA method results in 0.5123.Keywords : Naïve Bayes, Principal Component Analysis, Correlation-based Feature Selection, Confusion Matrix, Root Mean Square Error

Model Hybrid Menggunakan Dekomposisi-Neural Network Untuk Data Indeks Harga Saham Gabungan Imelda Saluza; Dewi Sartika; Ensiwi Munarsih
Jurnal Ilmiah Informatika Global Vol. 13 No. 3
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i3.2696

Â The development of Covid-19 has worsened the economy not only nationally but also globally. Since its spread, the price movement of the Jakarta Composite Index (IHSG) has continued to be volatile. JCI price volatility shows risk and uncertainty in investing. Volatility is used as a barometer to determine portfolio management strategies for financial actors. Therefore, financial actors should find a strategy to be able to predict JCI price movements to reduce risks and gain profits. One way that can be done is to predict the JCI price as a reference in investing. This study uses a hybrid model between the decomposition model and the Neural Network (NN) in predicting JCI price volatility. The decomposition uses two approaches, namely additive and multiplicative, the two approaches will then be combined with NN and the NN algorithm used is Feed Forward Neural Network (FFNN) where the results of the decomposition in the form of seasonal, trend, and random data are used as input in the FFNN architecture. The FFNN architecture in this study differs from the hidden layer nodes and the epochs used. Furthermore, the prediction results from the model are compared with a single NN. The performance of each architecture will be measured using the Mean Absolute Error (MAE) and Mean Square Error (MSE). The results show that the hidden layer with more nodes can provide good performance while the epoch used provides good performance depending on the learning process carried out. The prediction results using the hybrid model can outperform the performance of a single NN.Keywords : time series, volatilitas, studi perbandingan, kecerdasan buatan, statistik.

Sistem Informasi Pemesanan Kain Tradisonal Menggunakan Metode Agile Dengan Studi Kasus Rumah Songket Cek Unah Palembang Akbar, Muhamad Hafiz; Saluza, Imelda; Dhamayanti
Jurnal Ilmiah Informatika Global Vol. 16 No. 2: August 2025
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v16i2.5427

Rumah Songket Cek Unah Palembang is a local business that sells traditional Palembang fabrics such as songket, tanjung, and jumputan. This business faces obstacles in marketing, payment, and reporting that hinder the business process. This study aims to design a website-based ordering information system using the agile method, which is flexible in accommodating changing user needs. This system is equipped with product catalog features, online ordering with payment integration via e-wallet, and structured sales reports. Data collection methods are carried out through observation, interviews, and documentation, while system testing uses the black-box testing method. This system is designed to facilitate access to product information, allow customers to order from anywhere, improve operational efficiency, expand market share, and introduce the Rumah Songket Cek Unah brand as a pioneer of original Palembang songket fabrics in the digital era. In addition, this system facilitates sales monitoring and product management through optimal feature integration.

Sistem Informasi Pelayanan Surat Digital Kelurahan Berbasis Android Pada Kelurahan Sukajaya Palembang Anjani, Fia Sakina; Saluza, Imelda; Marcellina, Dona
Jurnal Ilmiah Informatika Global Vol. 16 No. 2: August 2025
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v16i2.5434

The advancement of information technology has driven various innovations in public services, one of which is through an Android-based digital system. This study aims to design and develop a Digital Mail Service Information System in Sukajaya Village, Palembang, to overcome the problems of long bureaucracy, lack of transparency, and limited public accessibility. This system provides solutions for administrative management, such as submitting domicile certificates, business certificates, and other letters, with superior features in the form of user registration, service submission, and real-time monitoring of application status. The methodology used is a prototype, which involves the stages of communication, planning, modeling, construction, and system testing. The system is designed using Java for Android application development and MySQL as a database. The test results show that this system is able to make it easier for the public to access services, reduce administrative process time, and increase the efficiency of village employees. This study concludes that the implementation of an Android-based information system can provide significant benefits, both for the community and the village government, in building modern and technology- based governance. This system is also expected to be a model for other villages in improving public services digitally.

Co-Authors , Hartati Abdul Aziz Zulfikar Agustina Heryati Ahmad Sanmorino Akbar, Muhamad Hafiz Alie, Muhammad Fadhiel Alpin Herman Saputra Andini Utari Putri Anggraini, Leriza Desitama Anjani, Fia Sakina Antoni, Darius Ariati, Nining Aulia, Beta Bagus Setiawan Bobby Halim Cyndika Dewi Sartika Dewi Sartika Dewi Sartika DEWI SARTIKA Dhamayanti Dhamayanti Dhamayanti Dhamayanti Dhamayanti Dhamayanti, Dhamayanti Eko Nugroho, Muhammad Davidio Endah Dewi Purnamasari Endah Purnamasari Endah Puspita Sari Ensiwi Munarsih, Ensiwi Eva Susanti Evi Yulianti EVI YULIANTI Faradilla Faradilla Faradillah , Faradillah Faradillah Faradillah Faradillah, Faradillah Fattah, Hussein HARTATI Hartati Hartati Hartati Hartati Hatika Hatika, Hatika Heryati , Agustina Heryati*, Agustina Husnawati Husnawati Iisnawati, Iisnawati Indah Permata Sari Indah Permatasari Indah Permatasari Indah Permatasari Indah, Sylvia Uly Kesuma, Hendra Di Lastri Astuti Lastri Widya Astuti Lastri Widya Astuti Lastri Widya Astuti, Lastri Widya Leriza Anggraini M. Fadhiel Alie Marcelina, Dona Marcellina, Dona Maya Amelia Mohammad Taufikurrahman Muhammad Fadhiel Alie Muhammad Haviz Irfani Nazori Suhandi Nining Nova Yanti Maleha Patriansah, Mukhsin Pratiwi, Indah Putri Putri, Hilda Muslia Putri, Indah Pratiwi Ramadhan, Mustafa Rini Yunita, Rini Roswaty Roswaty Roswaty Roswaty Roswaty Roswaty Roswaty Roswaty, Roswaty Rudi Heriansyah Rudi Heriansyah, Rudi Sartika, Dewi Sinta Habibah Sunardi, Hastha Suryati Syachrul Haq, Muhammad Raka Syahrul Haq, Muhammad Raka Teguh Teguh Wulandari, Try Yulianti, Evi Yulius, Yosef Zulfikar, Abdul Aziz

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