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All Journal International Journal of Electrical and Computer Engineering CommIT (Communication & Information Technology) Jurnal Teknologi Elektro JOIN (Jurnal Online Informatika) Jurnal Ilmiah KOMPUTASI Computer Science and Information Technologies Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Wulandhari, Lili Ayu
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks Qur'ania, Arie; Harsani, Prihastuti; Triastinurmiatiningsih, Triastinurmiatiningsih; Wulandhari, Lili Ayu; Gunawan, Alexander Agung Santoso
CommIT (Communication and Information Technology) Journal Vol 14, No 1 (2020): CommIT Vol. 14 No. 1 Tahun 2020
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v14i1.5952

Abstract

The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and P and P and K. The researchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.
Prediction of student performance at polytechnic using machine learning approach Hutajulu, Kristina; Wulandhari, Lili Ayu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5356-5365

Abstract

Educational data mining (EDM) is a strategic technique for exploring data in educational environments to gain a deeper understanding of education. One of the goals of EDM is to predict things related to students in the future which can be done using a machine learning approach. In this paper, a regression model is developed to predict student performance in the first semester and the waiting period for graduate employment using machine learning approach based on informatics management (MI) and non-informatics management (non-MI) student data. Four regression models are compared for predicting student performance in the first semester and waiting period for graduate employment, including support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and XGBoost regression. Based on the experiment, prediction of students' performance in the first semester, the highest R2 result produced by SVR model by value of 0.58 for MI and by RFR by value of 0.34 for non-MI. While, waiting period for graduate employment prediction, the highest R2 result produced by AdaBoost regression by value of 0.44 for MI and SVR by value of 0.39 for non-MI.
Prediksi Kinerja Calon Mahasiswa Berdasarkan Nilai Seleksi Masuk Menggunakan Pendekatan Machine Learning Ariyanto, Sisia Dika; Wulandhari, Lili Ayu
Jurnal Ilmiah Komputasi Vol. 23 No. 2 (2024): Jurnal Ilmiah Komputasi : Vol. 23 No 2, Juni 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.23.2.3589

Abstract

Politeknik Astra menerapkan tes seleksi masuk dimana salah satu tahapan utamanya adalah Tes Potensi Akademik (TPA). TPA terdiri dari tujuh subtes. Panitia seleksi berkeinginan untuk melakukan perubahan menjadi efektif dan efisien dengan mengurangi jumlah subtes berdasarkan subtes yang paling berpengaruh. Untuk mengetahui subtes yang paling berpengaruh terhadap performa mahasiswa, digunakanlah Machine Learning . Pendekatan dilakukan dengan algoritma klasifikasi dan regresi. Hasil dari klasifikasi, algoritma Random Forest memberikan hasil terbaik. Selanjutnya untuk melihat fitur yang paling berpengaruh terhadap kelulusan seleksi masuk, dilakukan seleksi fitur dengan metode filter dan impurity-based . Tiga fitur terbaik diperoleh dari Prodi MI dan Non-MI. Selanjutnya dilakukan regresi dengan dua algoritma, yaitu Support Vector Regression (SVR) dan Neural Network Regression dengan konfigurasi 3, 5, dan model 7 fitur. Hasil terbaik konsisten dengan dua model data yaitu algoritma SVR dengan mean absolute error untuk Prodi MI 0.17 dan Non-MI 0.19. Hasilnya, model data dengan 3 fitur memiliki hasil terbaik untuk Prodi MI, artinya TPA dapat disederhanakan dengan tiga fitur, sedangkan pada Prodi Non MI, hasil terbaik pada tujuh fitur.
Prediksi Keterserapan Siswa SMK Pada Dunia Industri Dengan Pendekatan Educational Data Mining Asih, Kuri; Wulandhari, Lili Ayu
Jurnal Teknologi Elektro Vol 15, No 1 (2024)
Publisher : Electrical Engineering, Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jte.2024.v15i1.011

Abstract

Tingginya tingkat penyerapan tenaga kerja siswa kejuruan sangat menentukan kualitas suatu Sekolah Menengah Kejuruan (SMK). Semakin banyak siswa yang terserap ke dunia kerja dan semakin cepat bekerja setelah lulus maka semakin baik bagi SMK tersebut. Data mining adalah solusi yang berguna untuk mengidentifikasi pola tersembunyi dan memberikan saran untuk meningkatkan kinerja siswa. Penelitian ini menggunakan data rapor dari 167 siswa jurusan Teknik Jaringan Komputer SMK Negeri 26 Jakarta selama enam semester, dari tiga angkatan siswa yang lulus tahun 2015 hingga 2017. Penelitian ini menggunakan model SVR dan ANN dan metode Mean Absolute Error (MAE). Hasil penelitian menunjukkan bahwa ANN dengan data seluruh fitur yang digunakan, dengan model normalisasi Standard Scaler, dan algoritma aktifasi Relu,  jumlah Neuron sebanyak 128 dan Iter Max 150 menunjukkan performa terbaik, yaitu MAE sebesar 2,2 bulan. Heatmap korelasi Pearson mengungkapkan bahwa semua mata pelajaran yang sangat erat hubungannya dan mempengaruhi jumlah serapan mahasiswa di dunia kerja adalah mata pelajaran produktif (vokasi) pada semester 1 & 2 pada aspek penilaian keterampilan (praktik). Untuk meningkatkan angka penyerapan tenaga kerja, mahasiswa harus mempertajam dan memperdalam kompetensi mata pelajaran praktik vokasi pada awal semester. Hasil penelitian ini dapat dijadikan acuan untuk memprediksi penyerapan lulusan SMK di dunia kerja dan sebagai langkah antisipatif untuk meningkatkan nilai kompetensi sebelum memasuki dunia kerja.
Cassava Diseases Classification using EfficientNet Model with Imbalance Data Handling Ngesthi, Stephany Octaviani; Wulandhari, Lili Ayu
JOIN (Jurnal Online Informatika) Vol 9 No 2 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i2.1300

Abstract

This research highlights the urgent need for classifying cassava diseases into five classes, such as Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Green Mottle (CGM), and Cassava Mosaic Disease (CMD), and Healthy. The study proposes the utilization of the EfficientNet model, a lightweight deep learning architecture, for classifying cassava diseases based on leaf images. However, the datasets available for this classification task are all unbalanced, made it difficult for researchers to perform. To tackle this imbalance issue, the authors compared several imbalance data handling methods commonly used for image classification, including SMOTE (Synthetic Minority Oversampling Technique), basic augmentation, and neural style transfer, to be applied before fed into EfficientNet. Initially, EfficientNet model without addressing dataset imbalances, the F1-Score stands at 78%, with most images misclassified into the majority class. Integration with SMOTE notably boosts the F1-Score to 82%, showcasing the efficacy of oversampling methods in enhancing model performance. Conversely, employing data augmentation, both basic and deep learning-based, lowers the F1-Score to 74% and 65% respectively, yet it results in a more balanced distribution of true positives across disease classes. The findings suggest that SMOTE surpasses the other methods in handling imbalanced data.
Company clustering based on financial report data using k-means Firdaus, Gusti Aditya Aromatica; Wulandhari, Lili Ayu
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p243-253

Abstract

Stock investment is the act of providing funds or assets to obtain future payments for gifts given. In its application, novice investors often make mistakes, one of which is not knowing the health condition of the company they want to target. By applying the machine learning clustering method based on company financial report data, it was found that 2 clusters were formed. This can show the current condition of the company so that it can be a consideration for investors, such as clusters of companies that have a profit trend that is always stable and increasing, or clusters of companies that are in the process of developing their business and groups of companies that have large amounts of debt from year to year.
E-Commerce Customer Churn Prediction Using Machine Learning Approaches Wibowo, Bagaskara Putra; Wulandhari, Lili Ayu
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.482

Abstract

E-commerce businesses face the challenge of retaining customers in the era of rapid digital expansion. Customer churn prediction becomes essential for strategic decision-making by offering insights into potential revenue loss and customer loyalty. One of the problem in customer churn prediction comes from the presence of outliers in the data. This research delves into seeing the effects on churn prediction f1-score by incorporating a combination of techniques including outlier detection via k-means clustering and DBSCAN, as well as employing XGBoost and Catboost as classifiers. Results indicate that using Catboost gives a better performance of 96% F1-Score for e-commerce customer churn dataset with outliers, and removing outliers does not result in an increase in performance
Co-Authors Alexander A. S. Gunawan, Alexander A. S. Arie Qur’ania Ariyanto, Sisia Dika Asih, Kuri Firdaus, Gusti Aditya Aromatica Hutajulu, Kristina Ngesthi, Stephany Octaviani Prihastuti Harsani Triastinurmiatiningsih, Triastinurmiatiningsih Wibowo, Bagaskara Putra