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All Journal TEKNIK INFORMATIKA Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Creative Information Technology Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURTEKSI Jurnal Sistem Informasi Kaputama (JSIK) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences Jurnal Dharma Bhakti Ekuitas Jurnal Minfo Polgan (JMP) Jurnal ICT : Information and Communication Technologies Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka PUBLIDIMAS
Lili Tanti, Lili
Universitas Potensi Utama
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DESIGN AND CONSTRUCTION OF SOIL MOISTURE DETECTION TOOL USING ANDROID BASED DECISION TREE ALGORITHM Aziz Ritonga, Mirwan; Tanti, Lili
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4194

Abstract

Abstract: Soil moisture is an important factor in determining the watering needs of plants for optimal growth. Therefore, accurate monitoring of soil moisture is necessary. This research aims to design and build a soil moisture detection tool based on the Decision Tree algorithm with the support of the YL-69 sensor for humidity measurement and the DHT11 sensor for temperature measurement to increase data accuracy. This system uses NodeMCU ESP8266 as a microcontroller and is integrated with an Android application as a user interface. Sensor interpretation data is analyzed using the Decision Tree algorithm to determine soil conditions (dry, damp or wet). The test results show an accuracy level of 95% from 300 data samples. Thus, this system is able to detect soil moisture effectively and can help increase the efficiency of crop management on a household and commercial agricultural scale. Keywords: agriculture, android, decision tree algorithm, sensors, soil moisture detection
Anomaly Detection in Computer Networks Using Isolation Forest in Data Mining Lubis, Hartati Tammamah; Roslina, Roslina; Tanti, Lili
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44285

Abstract

The rapid growth of network data has increased the complexity of detecting anomalies, which are crucial for ensuring the security and integrity of information systems. This study investigates the use of the Isolation Forest algorithm for anomaly detection in network traffic, utilizing the Luflow Network Intrusion Detection dataset, which contains 590,086 records with 16 features related to network activities. The methodology encompasses data preprocessing (cleaning, normalization, and feature scaling), feature selection (bytes in, bytes out, entropy, and duration), model training, and performance evaluation. The results demonstrate that Isolation Forest can effectively identify anomalies based on feature patterns, isolating suspicious data points without the need for labeled datasets. However, performance metrics, such as accuracy (42.92%), precision (14.37%), recall (2.87%), and F1-score (4.79%), reveal challenges such as high false-positive rates and low sensitivity to true anomalies. These findings highlight the potential of the algorithm for dynamic, high-dimensional datasets but also indicate the need for further improvements through hyperparameter tuning, feature engineering, and alternative approaches. This study contributes to the development of adaptive anomaly detection frameworks for network security and suggests future integration into real-time systems for proactive threat mitigation. The study's findings are particularly relevant for enhancing network security in environments such as corporate and governmental networks, where real-time anomaly detection is crucial.
Online Shop Product Sales Prediction Using Multilayer Perceptron Algorithm Safitri, Erica Rian; Tanti, Lili; Wanayumini, Wanayumini
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44286

Abstract

This study aims to develop a predictive model for forecasting product sales using the Multilayer Perceptron (MLP) algorithm. The model's performance was evaluated using key metrics, including the Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score. The model achieved an MAE of 0.861, an MSE of 9.521, and an impressive R² score of 0.999, demonstrating its ability to accurately predict product sales with minimal error. Feature correlation analysis identified key variables related to the target prediction, which is the number of products ready for shipment, underscoring the importance of feature selection in enhancing model performance. Prediction results revealed variability among product sales, with products like Foodpak Matte 245 (Code 49) predicted to sell approximately 244.31 units, while others like Stiker Kertas (Code 90) showed lower sales forecasts. The findings suggest that strategic interventions may be necessary to boost sales for underperforming items and capitalize on the demand for popular products. Future improvements, such as optimizing the network architecture, experimenting with activation functions and optimization algorithms, and incorporating external factors such as market trends, could further enhance the model’s accuracy and predictive power. Overall, the MLP model demonstrates strong potential for product sales forecasting, providing valuable insights for business decision-making.
PELATIHAN PRESENSI ONLINE MENGGUNAKAN GOOGLE FORM DI YAYASAN PENDIDIKAN ISLAM AR-RIDHA Adhar, Deni; Safrizal, Safrizal; Tanti, Lili; Fahrozi, Wirhan
Jurnal Pengabdian Masyarakat Sabangka Vol 3 No 03 (2024): Jurnal Pengabdian Masyarakat Sabangka
Publisher : Pusat Studi Ekonomi, Publikasi Ilmiah dan Pengembangan SDM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62668/sabangka.v3i03.1082

Abstract

The use of online attendance at several schools in the North Sumatra region can help efficiency in reducing physical materials (paper) and can be directly monitored/evaluated online. Currently, the school at the Ar-Ridha Foundation, one of the attendance methods implemented online, still uses WhatsApp to monitor online attendance. Due to many obstacles, sending attendance via WhatsApp is considered less effective. One of the barriers reported was the requirement for teachers to review each student's data submission individually. Using Google Forms to create virtual courses is one way teachers can apply online learning. Google Forms is a Google product. Google Forms is a free online program available to schools, non-profit organizations, and anyone with a Google Account. Using Google Forms will facilitate interaction between teachers and students. Google Forms makes it easy for students and teachers to stay in touch. Google Forms is a blended learning platform that Google developed for schools seeking to create, distribute, and assign paperless assignments
OPTIMIZING DECISION TREE PERFORMANCE WITH RECURSIVE FEATURE ELIMINATION FOR HIGH-DIMENSIONAL MUSHROOM CLASSIFICATION Tanti, Lili; Safrizal; Thanri, Yan Yang
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6816

Abstract

Classifying mushroom species presents a significant challenge within biological data analysis because of the wide variety of species and their distinct attributes. This research investigates the effectiveness of the Decision Tree classifier for mushroom categorization by comparing two splitting criteria, the Gini Index and Entropy. Additionally, the study employs the Recursive Feature Elimination (RFE) method for dimensionality reduction to enhance model efficiency and performance. The dataset was collected, cleaned, and analyzed exploratorily before feature selection was conducted using RFE. The Decision Tree model was trained and evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that applying RFE improved computational efficiency without compromising model accuracy. The Gini criterion provided more stable results across all metrics, while Entropy demonstrated higher precision in certain cases. Model optimization through parameter tuning produced the best parameter combination at max_depth = 5, min_samples_leaf = 5, and min_samples_split = 10. This study concludes that integrating RFE with the Decision Tree can significantly enhance the performance of high-dimensional dataset classification. The findings are expected to serve as a reference for developing efficient and accurate biological data classification models
OPTIMIZATION OF MLP-NN FOR MANGO LEAF DISEASE PREDICTION USING IMAGE-BASED FEATURE EXTRACTION Triandi, Budi; Tanti, Lili
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7031

Abstract

Mango (Mangifera indica Linn.) is a nutrient-rich fruit, yet leaf diseases caused by microorganisms can significantly reduce crop productivity. Early detection is essential to prevent further damage and support effective disease management. This study proposes an optimized mango leaf disease prediction model using a multi-layer perceptron neural network (MLP-NN). Image-based feature extraction is performed using the Inception v3 architecture to obtain high-level color and texture features that improve classification performance. Unlike previous studies that rely solely on manually engineered features or full CNN training, this research introduces a hybrid approach that integrates deep feature extraction with MLP-NN optimization, offering a lightweight yet highly accurate alternative. Several hyperparameter combinations, including activation functions (ReLU, tanh, sigmoid) and optimization algorithms (Adam and SGD), were evaluated using the Orange platform. The optimized MLP-NN model with ReLU and Adam achieved the highest accuracy of 93.5%, demonstrating better stability and training efficiency compared to other configurations. These findings highlight the novelty and advantages of the proposed method, showing improved accuracy with lower computational cost relative to many existing approaches. This study provides an efficient solution for mango leaf disease prediction and supports future development of automated plant disease detection systems
Optimasi Support Vector Machine Menggunakan Particle Swarm Optimization pada Analisis Sentimen Program Efisiensi Anggaran Pemerintah Nurhayati, Nurhayati; Tanti, Lili; Triandi, Budi
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.15929

Abstract

Respon publik terkait kebijakan pemerintah, termasuk program efisien anggaran dapat dipahami menggunakan pendekatan analisis sentimen. Penelitian ini bertujuan membandingkan kinerja algoritma Support Vector Machine (SVM) dengan kernel Radial Basis Functional (RBF), kernel Linear, dan kernel Polynomial, serta mengevaluasi pengaruh Particle Swarm Optimization (PSO) terhadap peningkatan performa klasifikasi. Dataset sebanyak 4274 data diperoleh melalui teknik crawling dari media sosial X (Twitter) dan kemudian diproses melalui langkah-langkah pembersihan teks seperti cleaning, case folding, normalization, tokenization, stopword removal, stemming, labeling menggunakan lexicon based serta menggunakan TF-IDF (Term Frequency-Inverse Document Frequency) untuk ekstraksi fitur. Proses penilaian kinerja model dilaksanakan dengan memanfaatkan indikator accuracy, precision, recall, dan F1-score, serta didukung oleh analisis confusion matrix. Berdasarkan hasil pengujian yang diperoleh, penelitian ini menunjukkan bahwa SVM kernel Linear berhasil meningkatkan akurasi menjadi 0.7579 atau 75.79%, sedangkan pada kernel RBF dan Polynomial tidak memberikan peningkatan signifikan. Selain itu, kelas netral menjadi kelas yang paling sulit diklasifikasikan. Penelitian ini menyimpulkan bahwa kombinasi SVM Linear dan PSO merupakan model terbaik untuk analisis sentimen kebijakan efisiensi anggaran pemerintah, serta menegaskan pentingnya pemilihan kernel dan strategi optimasi yang tepat dalam pengembangan sistem klasifikasi berbasis machine learning.
Perbandingan Algoritma Decision Tree dan Naive Bayes Pada Analisis Sentimen Masyarakat Terhadap Pejabat Pertamina Pasca Kasus Pertamax Oplosan Mubarak, Mubarak; Tanti, Lili; Rosnelly, Rika
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.15971

Abstract

Penelitian ini membandingkan kinerja algoritma pembelajaran mesin, yaitu Decision Tree dan Naive Bayes, dalam menganalisis sentimen masyarakat terhadap pejabat Pertamina setelah kasus "Pertamax Oplosan". Analisis sentimen merupakan alat penting untuk memahami opini publik dan manajemen krisis reputasi. Kasus Pertamax Oplosan memicu kontroversi publik yang luas, dan analisis sentimen terhadap tanggapan pejabat Pertamina dapat memberikan wawasan mengenai efektivitas strategi komunikasi krisis perusahaan.Algoritma Decision Tree menawarkan model berbasis pohon keputusan yang intuitif dan mudah diinterpretasi, meskipun rentan terhadap overfitting. Sebaliknya, Naive Bayes, dengan pendekatan probabilistiknya, dikenal efisien secara komputasi, terutama pada dataset besar. Penelitian ini bertujuan untuk mengukur kinerja kedua algoritma dalam mengklasifikasikan sentimen (positif, negatif, atau netral) dari data teks yang dikumpulkan dari media sosial. Data yang digunakan terbatas pada data teks di Twitter dengan kata kunci "Pertamax Oplosan" dan difokuskan pada sentimen terhadap pejabat Pertamina, bukan perusahaan secara keseluruhan. Data mentah sebanyak 3928 komentar tweet berhasil dikumpulkan melalui API Twitter. Metodologi penelitian ini mencakup beberapa tahapan, yaitu pengambilan data (crawling), preprocessing data, pelabelan pola sentimen, ekstraksi fitur, pembagian dataset, klasifikasi, dan evaluasi model. Data dibagi menjadi data latih (training) dan data uji (testing) dengan kombinasi 80:20. Hasil evaluasi akan menggunakan matriks kebingungan (confusion matrix) untuk mengukur akurasi, presisi, recall, F1-score, dan ROC Analysis. Hasil penelitian ini diharapkan dapat memberikan rekomendasi algoritma yang paling sesuai untuk analisis sentimen serupa dan menjadi panduan praktis bagi perusahaan dalam mengelola krisis reputasi.
Co-Authors adhar, Deni Adhar, Deni Adhar Aziz Ritonga, Mirwan Bob Subhan Riza, Bob Subhan Budi Triandi, Budi Hartama, Dedy Iwan Fitrianto Rahmad Juli Iriani, Juli Lubis, Hartati Tammamah Mubarak Mubarak Nurhayati Nurhayati Pairin, Yusfrizal Bin Poningsih Poningsih, Poningsih Ridho, Irdian Rika Rosnelly, Rika Riza, Bob Subahan Roslina Roslina, Roslina Safitri, Erica Rian Safrizal Safrizal Safrizal Salwani D, Zuki Salwani D Susianto Susianto Thanri, Yan Yang Wanayumini Windy, Micky Alviansyah Wirhan Fahrozi, Wirhan