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All Journal Jurnal Media Infotama Jurnal Media Computer Science Jurnal Komputer dan Teknologi (JUKOMTEK) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Nelly Khairani Daulay
Universitas Bina Insan
Computer Science & IT Library & Information Science

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ANALISIS SENTIMEN APLIKASI E-GOVERENMENT PADA GOOGLE PLAY MENGGUNKAN ALGORITMA NAÏVE BAYES Rini Sartina; Elmayati; Nelly Khairani Daulay
JURNAL ILMIAH BETRIK Vol. 14 No. 01 APRIL (2023): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v14i01 APRIL.29

Abstract

E-gov is one of the innovations created by the government to be able to compete in the digital world. One government agency that has used e-gov is the Ministry of Spatial Planning/National Land Agency (ATR/BPN). in this study the authors used the Naïve Bayes Algorithm as a classification method to determine positive, negative, and also neutral sentiments. The labeling process shows that the touch my land application tends to get a neutral response which can be seen from the comparison of 4000 data, 2265 positive, 1453 negative and 211 neutral data. and the results of sentiment analysis testing using the Naïve Bayes Algorithm produce an accuracy of 81%, 78% precision, and 79% recall.
Densenet201 Feature Extraction With Soft Voting Ensemble For Accurate Rice Leaf Disease Classification Nelly Khairani Daulay; Novi Lestari; Rusdiyanto Rusdiyanto
Jurnal Media Computer Science Vol 5 No 3 (2026): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i3.11876

Abstract

Rice leaf diseases are one of the major factors contributing to reduced agricultural productivity and economic losses for farmers. Manual disease identification generally requires expert knowledge and is often difficult to perform efficiently in field conditions. Therefore, this study aims to develop a rice leaf disease classification system by combining DenseNet201 as a feature extractor and a Voting Ensemble approach as the classifier. The dataset consisted of 1,470 rice leaf images categorized into five classes: Bacterial Leaf Blight, Brown Spot, Healthy Leaf, Leaf Blast, and Tungro. The dataset was divided using a stratified split strategy into 80% training data, 10% validation data, and 10% testing data. Image augmentation was applied only to the training set, increasing the number of training samples to 7,056 images. DenseNet201 was employed to extract image features into 1,920-dimensional feature vectors, which were subsequently classified using Logistic Regression, Support Vector Machine (SVM), Hard Voting, and Soft Voting. Experimental results showed that Logistic Regression achieved an accuracy of 95.24%, while SVM achieved 95.92%. Hard Voting obtained an accuracy of 95.24%, whereas Soft Voting achieved the best performance with an accuracy of 95.92%, precision of 95.75%, recall of 95.70%, F1-score of 95.71%, and ROC-AUC of 99.76%. Furthermore, the best-performing model was deployed in a Streamlit-based application for automatic rice leaf disease identification. The results demonstrate that the combination of DenseNet201 and Soft Voting provides an accurate and effective approach for rice leaf disease classification and has strong potential as an early disease detection tool in agriculture.
SISTEM PERAMALAN PENJUALAN KOPI BUBUK SELANGIT MENGGUNAKAN METODE WEIGHTED MOVING AVERAGE (WMA) MENGGUNAKAN DATA TIME SERIES BERBASIS FRAMEORK CI (CODEIGNITER) Glen Jupiter; Armanto Armanto; Nelly Khairani Daulay
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.706

Abstract

This study aims to forecast ground coffee sales using the Weighted Moving Average (WMA) method to support decision-making in business planning. The WMA method was selected because it assigns greater weight to recent historical data, thereby enabling a more responsive capture of changes in sales trends. The results indicate that ground coffee sales are projected to experience a stable upward trend in 2025. The model achieved a high level of accuracy, yielding a MAPE of 0.098%, an MAE of 54.463, and an RMSE of 70.683. Although discrepancies occurred in certain periods due to high sales volatility, the WMA method generally tracked actual data patterns effectively and produced realistic estimates. Consequently, the WMA method is a suitable tool for sales forecasting to support production planning, inventory control, and the formulation of more effective sales strategies.
ANALISIS PERBANDINGAN ALGORITMA DALAM MENEMUKAN POLA PEMBELIAN PRODUK PADA DATA PENJUALAN Antika dewi Asi; Budi Santoso; Nelly Khairani Daulay; Harma Oktafia Oktafia Lingga Wijaya
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10951

Abstract

The development of information technology in the Industry 4.0 era has driven significant changes in how organizations utilize data to support strategic decision-making. Data utilization is no longer limited to transaction recording but has shifted toward data processing as a source of predictive information that plays an important role in competitive business management. The urgency of this research is reinforced by the relatively low level of adoption of data-driven analytical systems among Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, including the outdoor equipment rental sector. In the modern business environment, decision-making can no longer rely solely on intuition but must be supported by data and predictive analysis to improve efficiency and competitiveness. Therefore, the development of a Smart Inventory Management system based on Business Intelligence, implementing the Apriori and FP-Growth algorithms at SAVANA Outdoor Store, is expected to provide automatic recommendations for inventory requirements based on real and representative historical transaction patterns. Based on the results of processing outdoor equipment rental data using the Apriori algorithm with a confidence value of 68%, several association rules were obtained, indicating a tendency of dissimilar borrowing patterns (mutually exclusive relationships) among certain types of equipment. Meanwhile, the processing results using the FP-Growth algorithm demonstrated better performance. This algorithm successfully generated a total of 21 association rules, with the top ten rules having confidence values ranging from 70% to 72%.
Co-Authors Antika dewi Asi Armanto Armanto Budi Santoso Elmayati Glen Jupiter Harma Oktafia Oktafia Lingga Wijaya Novi Lestari Rini Sartina Rusdiyanto Rusdiyanto