BINARY CLASSIFICATION USING SINGLE LAYER PERCEPTRON ON COMPUTER LAB ASSISTANT APPLICANT QUESTIONNAIRE DATA
Keywords:
Binary Classification, Artificial Neural Networks, Single Layer Perceptron Model, Pattern Recognition, Model TrainingAbstract
Students' interest in becoming computer lab assistants needs to be analyzed to understand the factors that influence it. This research uses a Single Layer Perceptron (SLP) Neural Network to perform binary classification on the questionnaire data of lab assistant applicants collected through Google Forms. The SLP model was trained with initial weights and biases of zero, a learning rate of 0.1, and a threshold of 0.5. The results show that within two epochs, the model was able to recognize patterns with an accuracy of 75%. This model has a precision of 100%, but a recall of only 50%, resulting in an F1 Score of 67%. These findings indicate that SLP can process questionnaire data well and has the potential to be applied to larger datasets to improve model performance.
Downloads
Download data is not yet available.
References
Pratama, H. K. (2011). Analisis pernbandingan pengenalan tanda tangan dengan menggunakan metode perceptron dan backpropagation.
Pangaribuan, Y., & Sagala, M. (2017). Menerapkan Jaringan Saraf Tiruan untuk Mengenali Pola Huruf Menggunakan Metode Perceptron. Jurnal Teknik Informatika Unika St. Thomas (JTIUST), 2(2), 53–59.
Wijanarto, W., & Puspitasari, R. (2019). Optimasi Algoritma Klasifikasi Biner dengan Tuning Parameter pada Penyakit Diabetes Mellitus. Eksplora Informatika, 9(1), 50–59. https://doi.org/10.30864/eksplora.v9i1.257
Nasution, D. A., Khotimah, H. H., & Chamidah, N. (2019). Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN. Computer Engineering, Science and System Journal, 4(1), 78. https://doi.org/10.24114/cess.v4i1.11458
Al-afghoni, J. M. H. Y., Setiawan, W., Dwi, Y., Negara, P., Informasi, S., Madura, U. T., Matrix, C., Tree, D., & Rusdiana, L. (2025). KLASIFIKASI JENIS BENIH KACANG MENGGUNAKAN SMOTE DAN DECISION TREE C4 . 5. 9(1), 462–469.
Bhakti, I. N., Sholikhin, A. Z., Abi Lukman, M., Daniati, E., & Ristyawan, A. (2024). Klasifikasi Kategori Berita Menggunakan Naive Bayes. Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 8(2), 1155–1164.
Qurnia, I. L., Prasetyo, E., Zainal, R. F., Studi, P., Informatika, T., Teknik, F., & Bhayangkara, U. (2016). CLASSIFICATION OF DIABETES DISEASE USING Case Study : 1(2), 147–151.
Medeiros, M. C., Varella, W. A., Viana, G. D., Mendes, A., & Neto, O. (2023). IMPLEMENTATION OF AN ALGORITHM FOR BINARY CLASSIFICATION OF HANDWRITTEN DIGITS USING SCILAB IMPLEMENTATION OF AN ALGORITHM FOR BINARY CLASSIFICATION. October. https://doi.org/10.5281/zenodo.10051944
Sari, N. R., & Mar’atullatifah, Y. (2023). PENERAPAN MULTILAYER PERCEPTRON UNTUK IDENTIFIKASI KANKER PAYUDARA. Jurnal Cakrawala Ilmiah, 2(8), 3261–3268. https://doi.org/10.31862/9785426311961
Budiman, Y. U. (2022). Identifikasi Kata Benda Dan Bukan Kata Benda Menggunakan Single Layer Perceptron Network. BULLET : Jurnal Multidisiplin Ilmu, 1(5), 759–768.
Teixeira Zavadzki de Pauli, S., Kleina, M., & Bonat, W. H. (2020). Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction. Annals of Data Science, 7(4), 613–628. https://doi.org/10.1007/s40745-020-00305-w
Sutojo, T., Mulyanto, E., & Suhartono, V. (2011). Kecerdasan Buatan. Andi.
Kusuma, D. T., Siswipraptini, P. C., & Sangadji, I. (2022). SISTEM SYARAF BUATAN MENGOLA DATA DENGAN SISTEM CERDAS. Universitas Brawijaya Press.
Puspitaningrum, D. (2004). PENGANTAR JARINGAN SARAF TIRUAN. Jurnal Tranformatika, 1(2), 124.
Afriyanti, L. (2010). Rancang Bangun Tool Untuk Jaringan Syaraf Tiruan ( Jst ) Model Perceptron. 2010(Snati), 85–90.
Yanto, M. (2017). Penerapan Jaringan Syaraf Tiruan Dengan Algoritma Perceptron Pada Pola Penentuan Nilai Status Kelulusan Sidang Skripsi. Jurnal Teknoif, 5(2), 79–87. https://doi.org/10.21063/jtif.2017.v5.2.79-87
Siswipraptini, P. C., Ruli, R., Siregar, A., Iriansyah, ;, Sangadji, B. M., Annisa, ;, & Wahyulia, S. (2022). Algoritma Perceptron Menggunakan Teknik Machine Learning Untuk Model Smart Distribution Beban Listrik. Jurnal Ilmiah, 14(2), 150–159.
Prasetyo, S. Y., & Nabiilah, G. Z. (2023). Perbandingan Model Machine Learning pada Klasifikasi Tumor Otak Menggunakan Fitur Discrete Cosine Transform. Jurnal Teknologi Terpadu, 9(1), 29–34. https://doi.org/10.54914/jtt.v9i1.605
Purwanto, A., & Nugroho, H. W. (2020). ANALISA PERBANDINGAN KINERJA ALGORITMA C4.5 DAN ALGORITMA K-NEAREST NEIGHBORS UNTUK KLASIFIKASI PENERIMA BEASISWA. Jurnal Teknoinfo, 14(1), 236–243. https://ejurnal.teknokrat.ac.id/index.php/teknoinfo/article/view/336/329
Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. Jurnal Sains Komputer & Informatika (J-SAKTI, 5(2), 697–711.
Dwi Fasnuari, H. A., Yuana, H., & Chulkamdi, M. T. (2022). Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Penyakit Diabetes Melitus. Antivirus : Jurnal Ilmiah Teknik Informatika, 16(2), 133–142. https://doi.org/10.35457/antivirus.v16i2.2445
Putri, I. P. (2021). Analisis Performa Metode K-Nearest Neighbor (KNN) dan Crossvalidation pada Data Penyakit Cardiovascular. Indonesian Journal Of Data And Science, 2(1), 21–28.
Muslihati, M., Sahibu, S., & Taufik, I. (2024). Implementasi Algoritma Convolutional Neural Network untuk Klasifikasi Jenis Sampah Organik dan Non Organik. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(3), 840–852. https://doi.org/10.57152/malcom.v4i3.1346
Wahyudi, R., Orisa, M., & Vendyansyah, N. (2021). Penerapan Algoritma K-Nearest Neighbors Pada Klasifikasi Penentuan Gizi Balita (Studi Kasus Di Posyandu Desa Bluto). JATI (Jurnal Mahasiswa Teknik Informatika), 5(2), 750–757.
Pangaribuan, Y., & Sagala, M. (2017). Menerapkan Jaringan Saraf Tiruan untuk Mengenali Pola Huruf Menggunakan Metode Perceptron. Jurnal Teknik Informatika Unika St. Thomas (JTIUST), 2(2), 53–59.
Wijanarto, W., & Puspitasari, R. (2019). Optimasi Algoritma Klasifikasi Biner dengan Tuning Parameter pada Penyakit Diabetes Mellitus. Eksplora Informatika, 9(1), 50–59. https://doi.org/10.30864/eksplora.v9i1.257
Nasution, D. A., Khotimah, H. H., & Chamidah, N. (2019). Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN. Computer Engineering, Science and System Journal, 4(1), 78. https://doi.org/10.24114/cess.v4i1.11458
Al-afghoni, J. M. H. Y., Setiawan, W., Dwi, Y., Negara, P., Informasi, S., Madura, U. T., Matrix, C., Tree, D., & Rusdiana, L. (2025). KLASIFIKASI JENIS BENIH KACANG MENGGUNAKAN SMOTE DAN DECISION TREE C4 . 5. 9(1), 462–469.
Bhakti, I. N., Sholikhin, A. Z., Abi Lukman, M., Daniati, E., & Ristyawan, A. (2024). Klasifikasi Kategori Berita Menggunakan Naive Bayes. Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 8(2), 1155–1164.
Qurnia, I. L., Prasetyo, E., Zainal, R. F., Studi, P., Informatika, T., Teknik, F., & Bhayangkara, U. (2016). CLASSIFICATION OF DIABETES DISEASE USING Case Study : 1(2), 147–151.
Medeiros, M. C., Varella, W. A., Viana, G. D., Mendes, A., & Neto, O. (2023). IMPLEMENTATION OF AN ALGORITHM FOR BINARY CLASSIFICATION OF HANDWRITTEN DIGITS USING SCILAB IMPLEMENTATION OF AN ALGORITHM FOR BINARY CLASSIFICATION. October. https://doi.org/10.5281/zenodo.10051944
Sari, N. R., & Mar’atullatifah, Y. (2023). PENERAPAN MULTILAYER PERCEPTRON UNTUK IDENTIFIKASI KANKER PAYUDARA. Jurnal Cakrawala Ilmiah, 2(8), 3261–3268. https://doi.org/10.31862/9785426311961
Budiman, Y. U. (2022). Identifikasi Kata Benda Dan Bukan Kata Benda Menggunakan Single Layer Perceptron Network. BULLET : Jurnal Multidisiplin Ilmu, 1(5), 759–768.
Teixeira Zavadzki de Pauli, S., Kleina, M., & Bonat, W. H. (2020). Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction. Annals of Data Science, 7(4), 613–628. https://doi.org/10.1007/s40745-020-00305-w
Sutojo, T., Mulyanto, E., & Suhartono, V. (2011). Kecerdasan Buatan. Andi.
Kusuma, D. T., Siswipraptini, P. C., & Sangadji, I. (2022). SISTEM SYARAF BUATAN MENGOLA DATA DENGAN SISTEM CERDAS. Universitas Brawijaya Press.
Puspitaningrum, D. (2004). PENGANTAR JARINGAN SARAF TIRUAN. Jurnal Tranformatika, 1(2), 124.
Afriyanti, L. (2010). Rancang Bangun Tool Untuk Jaringan Syaraf Tiruan ( Jst ) Model Perceptron. 2010(Snati), 85–90.
Yanto, M. (2017). Penerapan Jaringan Syaraf Tiruan Dengan Algoritma Perceptron Pada Pola Penentuan Nilai Status Kelulusan Sidang Skripsi. Jurnal Teknoif, 5(2), 79–87. https://doi.org/10.21063/jtif.2017.v5.2.79-87
Siswipraptini, P. C., Ruli, R., Siregar, A., Iriansyah, ;, Sangadji, B. M., Annisa, ;, & Wahyulia, S. (2022). Algoritma Perceptron Menggunakan Teknik Machine Learning Untuk Model Smart Distribution Beban Listrik. Jurnal Ilmiah, 14(2), 150–159.
Prasetyo, S. Y., & Nabiilah, G. Z. (2023). Perbandingan Model Machine Learning pada Klasifikasi Tumor Otak Menggunakan Fitur Discrete Cosine Transform. Jurnal Teknologi Terpadu, 9(1), 29–34. https://doi.org/10.54914/jtt.v9i1.605
Purwanto, A., & Nugroho, H. W. (2020). ANALISA PERBANDINGAN KINERJA ALGORITMA C4.5 DAN ALGORITMA K-NEAREST NEIGHBORS UNTUK KLASIFIKASI PENERIMA BEASISWA. Jurnal Teknoinfo, 14(1), 236–243. https://ejurnal.teknokrat.ac.id/index.php/teknoinfo/article/view/336/329
Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. Jurnal Sains Komputer & Informatika (J-SAKTI, 5(2), 697–711.
Dwi Fasnuari, H. A., Yuana, H., & Chulkamdi, M. T. (2022). Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Penyakit Diabetes Melitus. Antivirus : Jurnal Ilmiah Teknik Informatika, 16(2), 133–142. https://doi.org/10.35457/antivirus.v16i2.2445
Putri, I. P. (2021). Analisis Performa Metode K-Nearest Neighbor (KNN) dan Crossvalidation pada Data Penyakit Cardiovascular. Indonesian Journal Of Data And Science, 2(1), 21–28.
Muslihati, M., Sahibu, S., & Taufik, I. (2024). Implementasi Algoritma Convolutional Neural Network untuk Klasifikasi Jenis Sampah Organik dan Non Organik. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(3), 840–852. https://doi.org/10.57152/malcom.v4i3.1346
Wahyudi, R., Orisa, M., & Vendyansyah, N. (2021). Penerapan Algoritma K-Nearest Neighbors Pada Klasifikasi Penentuan Gizi Balita (Studi Kasus Di Posyandu Desa Bluto). JATI (Jurnal Mahasiswa Teknik Informatika), 5(2), 750–757.
Downloads
Published
2025-06-20
How to Cite
[1]
Nur Shafwa Aulia Sitorus and Armansyah, “BINARY CLASSIFICATION USING SINGLE LAYER PERCEPTRON ON COMPUTER LAB ASSISTANT APPLICANT QUESTIONNAIRE DATA”, antivirus, vol. 19, no. 1, pp. 113–123, Jun. 2025.
Issue
Section
Articles
License
Copyright (c) 2025 Antivirus : Jurnal Ilmiah Teknik Informatika
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
