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All Journal Teknosains: Media Informasi Sains dan Teknologi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal Sistem informasi dan informatika (SIMIKA)
Lapendy, Jessica Crisfin
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Physics

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KLASIFIKASI RASA JERUK SIAM BERDASARKAN WARNA DAN TEKSTUR BERBASIS PENGOLAHAN CITRA DIGITAL Lapendy, Jessica Crisfin; Resky, Andi Aulia Cahyana; Makmur, Haerunnisya; Kaswar, Andi Baso; Andayani, Dyah Darma; Adiba, Fhatiah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 2 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i2.5384

Abstract

Jeruk merupakan salah satu buah yang sangat populer di kalangan masyarakat Indonesia karena memiliki rasa yang segar, enak, dan memiliki banyak manfaat bagi kesehatan. Kandungan vitamin C yang melimpah membuat buah ini banyak dijadikan sebagai suplemen kesehatan sehingga jeruk memiliki nilai komersial dan pangsa pasar yang besar. Untuk mendapatkan manfaat yang maksimal dari buah ini, diperlukan kualitas jeruk yang baik, dilihat dari segi rasa dan tingkat kematangan buah jeruk. Salah satu jenis jeruk yang populer adalah jeruk siam. Akan tetapi, dari segi rasa buah jeruk asam dan manis masih sulit untuk dibedakan jika hanya dilihat oleh mata. Oleh karena itu, pada penelitian ini diusulkan sistem klasifikasi rasa buah jeruk siam berdasarkan warna dan tekstur kulit menggunakan jaringan syaraf tiruan berbasis pengolahan citra digital. Pada penelitian ini, rasa jeruk dibagi ke dalam 2 kelas, yaitu manis dan asam. Metode yang diusulkan terdiri atas 7 tahapan utama yaitu tahap akuisisi citra, preprocessing, segmentasi menggunakan Otsu Thresholding, penghilangan noise citra biner menggunakan K-Means, operasi morfologi, ekstraksi fitur warna serta tekstur, dan klasifikasi menggunakan jaringan syaraf tiruan. Beberapa skenario pengujian dilakukan dan diperoleh skenario penggabungan fitur warna LAB dengan fitur tekstur contrast, correlation, energy dan homogeneity yang menghasilkan akurasi tertinggi. Adapun nilai akurasi, precision, dan recall yang diperoleh, yaitu 98,75%, 100%, dan 97,56%. Hal ini menunjukkan bahwa metode yang diusulkan memiliki kinerja yang baik dalam mengklasifikasian rasa buah jeruk ke dalam kelas manis atau asam.
KLASIFIKASI TINGKAT KESEGARAN DAUN BAWANG MENGGUNAKAN JARINGAN SYARAF TIRUAN BERBASIS PENGOLAHAN CITRA DIGITAL Novianti, Andi Fitri; Atthariq, Muhammad; Dini, Juliano Nufiansyach; Kaswar, Andi Baso; Lapendy, Jessica Crisfin
Jurnal Sistem Informasi dan Informatika (Simika) Vol 7 No 2 (2024): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v7i2.3378

Abstract

Green onions, commonly used in Indonesian cuisine, have significant agricultural potential. Despite high production, their quality, particularly freshness, is traditionally evaluated visually, leading to inconsistent and subjective results. This study aims to develop an objective and accurate method for classifying the freshness of green onions using an Artificial Neural Network (ANN). Previous studies have employed ANN but have not specifically targeted the freshness classification of leeks. The proposed method utilizes the color and texture features of green onions.The research methodology includes image acquisition, preprocessing, segmentation, morphology, feature extraction, and classification using ANN. A total of 300 images were acquired and categorized into three freshness levels: not fresh, less fresh, and fresh. During the training phase, 240 images were used, and 80 images were reserved for testing. The optimal feature combination identified includes HSV and LAB color features along with texture features (Contrast + Energy). The results demonstrated that the freshness classification of green onions achieved 100% accuracy in both training and testing phases. The training process, with 240 images, had a computation time of 142.684 seconds, while the testing process, with 80 images, took 35.648 seconds. These findings indicate that using ANN based on color and texture features is highly effective in determining the freshness level of green onions.
Klasifikasi tingkat kematangan buah jeruk nipis (Citrus aurantifolia) menggunakan metode jaringan saraf tiruan berbasis citra digital Nurhidayat, Nurhidayat; Bugdady, Andi Jaedil; Dhanendra, Fadhil; Kaswar, Andi Baso; Lapendy, Jessica Crisfin
Teknosains Vol 18 No 3 (2024): September-Desember
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/teknosains.v18i3.47707

Abstract

Jeruk nipis merupakan warisan budaya Indonesia yang telah diturunkan berabad-abad. Buah Jeruk nipis memiliki banyak vitamin terutama vitamin C yang dianggap bermanfaat signifikan kepada tubuh. Penelitian ini bertujuan untuk mengetahui tingkat kematangan buah jeruk nipis (Citrus aurantifolia) menggunakan citra digital. Penelitian ini dilakukan pada bulan Maret 2024. Penelitian ini menggunakan metode jaringan saraf tiruan (JST) dengan pengambilan citra jeruk nipis untuk mendeteksi tingkat kematangan buah jeruk nipis yang dibagi menjadi dua data set uji dan latih. Hasil penelitian menunjukkan bahwa metode ini mencapai tingkat akurasi sebesar 87% pada tahap pelatihan dan 68% pada tahap pengujian. Waktu komputasi yang dibutuhkan untuk mengklasifikasikan satu citra adalah 207,36 detik pada tahap pelatihan dan 42,15 detik pada tahap pengujian.
Optimizing Sentiment Analysis of Electric Vehicles Through Oversampling Techniques on YouTube Comments Lapendy, Jessica Crisfin; Resky, Andi Aulia Cahyana; Tenriola, Andi; Surianto, Dewi Fatmarani; Sidin, Udin Sidik
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.88205

Abstract

Air pollution from motorized fuel vehicles causes adverse impacts on the environment and human health, driving the need for more sustainable alternatives such as electric vehicles. However, the transition to electric vehicles is often met with mixed responses from the public, reflected by sentiments that are split between positive and negative. This research investigates such sentiments through analyzing comments on the YouTube platform, which are classified using two algorithms, SVM and Naïve Bayes, and three oversampling techniques: Random Oversampling, SMOTE, and ADASYN. A comparative evaluation is conducted to determine the most effective algorithm and oversampling strategy for handling imbalanced sentiment data, where negative comments dominate. Initial experiments showed that Naïve Bayes with SMOTE achieved the best result among baseline models, with 64% accuracy. However, traditional oversampling methods alone were not sufficient to significantly improve classification quality. To address this, the study proposes a hybrid method that combines Easy Data Augmentation (EDA), specifically Synonym Replacement (SR), with oversampling techniques. The proposed method substantially improved performance. Naïve Bayes combined with SR and SMOTE or Random Oversampling achieved 88% accuracy, with F1-scores of 0.84–0.85 for the positive class. The best result was obtained using SVM with SR and Random Oversampling, reaching 97% accuracy and F1-scores of 0.97 (negative) and 0.96 (positive). These findings demonstrate the effectiveness of combining augmentation and oversampling in improving sentiment classification and provide insights for stakeholders in promoting EV adoption.
PCA and t-SNE Implementation for KNN Hypertension Classification Visualization Cahyana Resky, Andi Aulia; Lapendy, Jessica Crisfin; Nur Risal, Andi Akram; Surianto, Dewi Fatmarani; Wahid, Abdul
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6208

Abstract

Hypertension is a condition that, if allowed to increase, can significantly injure internal organs due to high blood pressure. The objective of this study is to use the K-Nearest Neighbor (KNN) algorithm along with PCA and t-SNE to accurately identify four categories of Hypertension, Normal, Hypertension, Stage 1 Hypertension, and Stage 2 Hypertension. After establishing the scope, a dataset consisting of 7,794 samples was sourced from Labuang Baji Regional General Hospital, Makassar, and contained age, weight, and systolic and diastolic blood pressure parameters. The class distribution is Normal (36.3%), Hypertension (43.12%), Stage 1 Hypertension (8.29%), and Stage 2 Hypertension (12.31%). Experimental results show that the KNN base model achieved 99% accuracy, KNN with PCA reached 100%, and KNN with t-SNE attained 99%. Cross-validation was used to evaluate model generalization, yielding accuracies of 91%, 94%, and 91%, respectively. These findings suggest that KNN, particularly when integrated with t-SNE, is highly effective in visualizing and classifying non-linear data structures. Furthermore, this study demonstrates that incorporating dimensionality reduction techniques enhances the interpretability of classified hypertension data, which is crucial for informed decision-making by mental health committees.
Analisis Metode Fuzzy C-Means (FCM) dalam Menentukan Performansi Kinerja Karyawan Lapendy, Jessica Crisfin; Resky, Andi Aulia Cahyana; Surianto, Dewi Fatmarani
TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol Vol 11, No 1 (2025): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v11n1.29-41

Abstract

Tercapainya sasaran perusahaan di setiap tahunnya dipengaruhi oleh kualitas sumber daya manusia atau karyawan yang dimiliki oleh perusahaan terkait. Kualitas ini berkaitan dengan kompetensi yang dimilikinya, baik itu dalam aspek skill maupun knowledge. Untuk melihat kualitas dari karyawan yang ada di perusahaan terkait, perlu dilakukan penilaian performansi kinerja karyawan. Oleh karena itu, penelitian ini bertujuan untuk menilai performansi kinerja karyawan yang ada di salah satu perusahaan swasta Makassar yang sebelumnya melakukan penilaian dengan melihat dari segi keuangan dan program kerja yang berhasil dipenuhi oleh setiap divisi. Perlunya penilaian kinerja adalah agar dapat membantu Human Resource Development (HRD) ataupun manajer dalam mengambil keputusan yang berkaitan dengan prestasi yang telah dicapai oleh setiap karyawan. Dalam penilaian kinerja karyawan ini digunakan metode Fuzzy C-Means yang merupakan teknik pengklasteran data yang ditentukan oleh derajat keanggotaan. Setelah tahapan-tahapan metode penelitian dilakukan dengan menggunakan Matlab, dihasilkan 3 klaster yang mengelompokkan kualitas karyawan menjadi karyawan dengan performansi kinerja baik sebanyak 11 karyawan, kinerja sedang sebanyak 11 karyawan, dan kinerja buruk sebanyak 6 karyawan. Hasil pengklasteran tersebut didasarkan pada hasil pengolahan data dari 5 kriteria penilaian, yaitu kejujuran, kedisiplinan, kepemimpinan, kehadiran, dan kualitas kerja. Di antara kelima kriteria tersebut, terdapat 2 kriteria yang cukup mempengaruhi hasil penilaian performansi kinerja karyawan di perusahaan terkait, yaitu kepemimpinan dan kualitas kerja. Adapun hasil evaluasi jumlah klaster dilakukan menggunakan metode silhouette coefficient dengan nilai tertinggi didapatkan yakni 0,5653 pada jumlah klaster adalah 3. The achievement of company goals each year is influenced by the quality of human resources or employees owned by the company. This quality is related to the competence they have, both in terms of skills and knowledge. To see the quality of employees in related companies, it is necessary to assess employee performance. Therefore, this study aims to assess the performance of existing employees in one of Makassar's private companies that previously conducted an assessment by looking at the financial aspects and work programs that were successfully fulfilled by each division. The need for performance appraisal is to be able to help Human Resource Development (HRD) or managers in making decisions related to the achievements that have been achieved by each employee. In this employee performance assessment, the Fuzzy C-Means method is used, which is a data clustering technique determined by the degree of membership. After the stages of the research method were carried out using Matlab, 3 clusters were produced which grouped the quality of employees into employees with good performance as many as 11 employees, moderate performance as many as 11 employees, and poor performance as many as 6 employees. The clustering results are based on the results of data processing from 5 assessment criteria, namely honesty, discipline, leadership, attendance, and work quality. Among the five criteria, there are 2 criteria that are quite influential in the results of employee performance assessment in related companies, namely leadership and work quality. The results of evaluating the number of clusters are carried out using the silhouette coefficient method with the highest value obtained, namely 0.5653 at the number of clusters is 3.
Co-Authors Abdul Wahid Adiba, Fhatiah Andi Baso Kaswar Andi Baso Kaswar Atthariq, Muhammad Bugdady, Andi Jaedil Cahyana Resky, Andi Aulia Dewi Fatmarani Surianto Dhanendra, Fadhil Dini, Juliano Nufiansyach Makmur, Haerunnisya Novianti, Andi Fitri Nur Risal, Andi Akram Nurhidayat Nurhidayat Resky, Andi Aulia Cahyana Tenriola, Andi Udin Sidik Sidin