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Kusrini, Kusrini

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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Perbandingan Kinerja Support Vector Machine dalam Klasifikasi Obesitas dengan Pendekatan Kernel Linear dan Radial Basis Function S, Muhamad Rois; Kusrini, Kusrini
Device Vol 15 No 1 (2025): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v15i1.8903

Obesitas adalah kondisi medis yang ditandai dengan penumpukan lemak tubuh yang berlebihan hingga dapat menimbulkan risiko berbagai penyakit kronis, seperti diabetes, penyakit jantung, dan kanker. Di Indonesia, dalam kurun waktu 10 tahun terjadi peningkatan obesitas yang signifikan, dari 10,5% pada tahun 2007 menjadi 21,8% pada tahun 2018. Secara global, pada tahun 2030 diperkirakan 1 dari 5 wanita dan 1 dari 7 pria akan hidup dengan obesitas, yang setara dengan lebih dari 1 miliar orang di seluruh dunia. Untuk mengatasi permasalahan ini, teknologi kecerdasan buatan digunakan dalam prediksi obesitas guna mengidentifikasi faktor risiko secara lebih akurat. Penelitian ini membandingkan performa klasifikasi obesitas menggunakan algoritma Support Vector Machine (SVM) dengan dua pendekatan berbeda: SVM dengan kernel Linear tanpa hyperparameter tuning dan SVM dengan kernel Radial Basis Function (RBF) dengan hyperparameter tuning. Dataset yang digunakan bersumber dari Universitas Sinop yang tersedia di Kaggle, dengan total 1610 data. Hasil eksperimen menunjukkan bahwa model SVM Linear tanpa hyperparameter tuning memiliki akurasi 72% pada data uji, sedangkan model SVM-RBF dengan hyperparameter tuning C dan gamma mencapai akurasi 83%. Perbedaan performa ini menunjukkan bahwa pemilihan kernel dan penerapan hyperparameter tuning dapat meningkatkan akurasi serta keandalan prediksi obesitas.

Klasifikasi Penyakit Diabetes Melitus Menggunakan Metode Stacking Ensemble Herlinawati, Noor; Kusrini, Kusrini
Device Vol 15 No 1 (2025): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v15i1.9331

Pendeteksian dini terhadap risiko diabetes merupakan tantangan penting dalam dunia medis modern. Penelitian ini bertujuan untuk meningkatkan akurasi klasifikasi pasien diabetes menggunakan metode stacking ensemble, yang menggabungkan tiga model pembelajaran mesin: K-Nearest Neighbors (KNN), Random Forest, dan XGBoost. Dataset yang digunakan adalah Pima Indians Diabetes, yang terdiri dari 768 data pasien. Setelah dilakukan preprocessing, balancing, dan feature selection, model stacking dibangun dengan Logistic Regression sebagai meta-learner. Hasil evaluasi menunjukkan bahwa stacking ensemble mencapai akurasi 77.27% dan ROC AUC 82.91%. Metode ini menunjukkan potensi besar dalam pengembangan sistem diagnosis otomatis yang lebih andal untuk penyakit diabetes..

Comparative performance of LSTM and DNN in sentiment analysis Tampubolon, Jandri; Kusrini, Kusrini
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i4.403

Understanding public sentiment toward online transportation services through social media analysis has gained increasing importance. This study provides a comparison between the effectiveness of Deep Neural Network (DNN), and Long Short-Term Memory (LSTM) models in analyzing user sentiment toward online transportation services in Indonesia using Twitter data. The dataset consists of 10,000 tweets related to Gojek, Grab, Maxim, and InDrive, collected from January to December 2023. Data preprocessing includes noise removal, case folding, tokenization, and stemming. Sentiment labeling was conducted using IndoBERTweet and manually validated. Using K-Fold Cross-Validation, both DNN and LSTM models were trained, and assessed using performance metrics such as accuration, precision, recall, and F1-score, training time, and Mean Absolute Error (MAE). The LSTM model demonstrated superior performances with accuration of 82,15%, precision of 82,21%, recall of 82,15%, specificity of 90,74%, F1-score of 82,10%, and MAE of 23,15%, compared to the DNN model which achieved an accuracy of 81,22%, precision of 81,20%, recall of 81,22%, specificity of 90,18%, F1-score of 81,12%, and MAE of 24,46%. However, DNN outperformed LSTM in training time efficiency (50,435 seconds vs. 148,765 seconds). LSTM shows significant advantages in understanding context and word relationships in sentiment analysis, while DNN offers better computational efficiency. The findings of this study can be utilized by online transportation services providers to improve service quality based on user feedback from social media.

Implementation of topsis algorithm for evaluating lecturer performance Fatkhurrochman, Fatkhurrochman; Kusrini, Kusrini; Alfatta, Hanif
International Journal Artificial Intelligent and Informatics Vol 1, No 1 (2018)
Publisher : Research and Social Study Institute (ReSSI)

Higher education is an education unit that able to carry out academic, professional and / or vocational programs. Lecturers are professional and scientific educators with the main task of transforming and developing, and disseminating science, technology and art through education, research and community service. The performance evaluation applies seven criterias, namely: attendance, teaching, research, dedication, loyalty, cooperation and responsibility. The problems are; the lecturersâ€™ performance evaluation is not optimal yet because there is no specific method to implement it. Therefore, it is necessary to build a decision support system by applying the Technique For Others Reference by Similarity to Ideal Solution (TOPSIS). This system will later help us in determining the best lecturer in accordance with the regulation. The TOPSIS method uses the principle that the chosen alternative must have the longest (farthest) distance from the negative ideal solution from geometric point of view using the relative proximity of an alternative. The alternative means the lecturersâ€™ performance with predetermined criterias. This method produces a lecturers rankings based on the best performance on numerical scores and sorted by the greatest preference scores. The particular study used 5 lecturers as alternative to be tested. They were Lecturers 1, Lecturers 2, Lecturers 3, Lecturers 4, and Lecturers 5. The results showed that Lecturer 1 was the best lecturer with the biggest preference score of 0.612.

Determination of receipt of UPZ assistance using the SAW method Listyanto, Ahmad Wildan; Kusrini, Kusrini; Sudarmawan, Sudarmawan
International Journal Artificial Intelligent and Informatics Vol 1, No 1 (2018)
Publisher : Research and Social Study Institute (ReSSI)

Zakat has a very strategic position in giving the impact of welfare and improving the community's economy if the collection and distribution are managed in trustworthy, transparent and professional. An institution on zakat management is National Zakat Agency (BAZNAS), which an institution manages the zakat management nationally, and non-structural government institution that is independent and responsible to President through the Minister of Religion. BAZNAS is located in capital city and assisted by zakat collection unit (UPZ). Along this time, they conduct manually in determining the person who will receive the assistance, but it often occur errors in its implementation. Therefore, a decision support system is needed to assist UPZ in determining the person who will receive the assistance. The decision support system was designed using SAW (Simple Additive Weighting) method, then the administrators obtain an alternative data in the form of lists of students who receive the UPZ assistance. The SAW algorithm is an algorithms for decision making. The SAW algorithm is also known as an algorithm with additive weighting method. The method requires normalization process of decision matrix (x) into a scale and can be compared to the entire available alternative ratings. The study showed that SAW can be applied to determine the acceptance of UPZ assistance by the calculation results of 16. The result of calculation and recommendation of decision support system for person who receive the UPZ acceptance, have the same data on output, namely Astin Dwi Wulan.

Optimizing rice leaf disease classification through convolutional neural network architectural modification and augmentation techniques Firdaus, Mohamad; Kusrini, Kusrini; Agastya, I Made Artha; Martínez-Béjar, Rodrigo
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3429-3438

This research focuses on advancing the accuracy of rice leaf disease classification through the integration of convolutional neural network (CNN) and deep learning models. With Indonesia ranking third in global rice production, effective crop management is crucial for sustaining agricultural output. This study employs innovative data augmentation techniques, including random zoom and others, to enhance model training robustness. The experimentation involves eight scenarios with varied architectural configurations applied to a residual network-50 (ResNet50) layers model, aiming to optimize disease classification performance. Featuring random zoom without the multilayer perceptron (MLP) component, emerges as the most effective, demonstrating superior accuracy and performance metrics. A grid search is conducted to optimize MLP layers, revealing a three-layer configuration as most effective. We found that the data augmentation and MLP layer can increase the accuracy of the disease classification task. The method proposed in this study is likely to have a much higher proportion of correct disease classification by combining MLP and zoom augmentation. Specifically, the model with three MLP layers and zoom augmentation demonstrated significantly higher accuracy, achieving a test accuracy, precision, recall, and F1-score of 0.92, 0.94, 0.92, and 0.92, respectively.

Perbandingan Performansi Algoritma Multiple Linear Regression dan Multi Layer Perceptron Neural Network dalam Memprediksi Penjualan Obat: Comparison of the Performance of Multiple Linear Regression Algorithms and Multi Layer Perceptron Neural Networks in Predicting Drug Sales Arifuddin, Danang; Kusrini, Kusrini; Kusnawi, Kusnawi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1952

Penelitian ini mengevaluasi pemilihan atribut dari variabel internal (jumlah penjualan) dan eksternal (cuaca, harga komoditas, inflasi) menggunakan metode korelasi, serta membandingkan performansi algoritma Multiple Linear Regression (MLR) dan Multi-Layer Perceptron Neural Network dengan backpropagation (MLPNN-b) dalam memprediksi penjualan obat analgesik di “Apotek XYZ”. Metrik evaluasi Mean Squared Error (MSE) dan Mean Absolute Percentage Error (MAPE) digunakan untuk mengukur akurasi prediksi. Hasil menunjukkan bahwa atribut internal "h-7" memiliki korelasi tertinggi (0,35) terhadap penjualan harian, sementara variabel eksternal seperti suhu harian, harga bawang merah, dan suku bunga juga memberikan kontribusi. Algoritma MLPNN-b dengan parameter tertentu mencapai MAPE 22,3% dan MSE 19.588 pada atribut tunggal, sedangkan MLR memiliki kinerja lebih merata pada atribut kombinasi dengan MAPE 25,6% dan MSE 22.768. Namun, kedua model masih mengalami underfitting dengan tingkat kesalahan prediksi yang cukup tinggi. Penelitian ini menyimpulkan bahwa meskipun MLPNN lebih unggul dalam menangkap hubungan non-linear dibandingkan MLR, akurasi prediksi masih belum optimal. Oleh karena itu, eksplorasi model hybrid serta integrasi lebih banyak variabel eksternal direkomendasikan untuk meningkatkan prediksi penjualan dan mendukung sistem manajemen stok farmasi yang lebih akurat.

Analisis Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Untuk Klasifikasi Cyberbullying Pada Sosial Media Twitter Maradona, Maradona; Kusrini, Kusrini; Alva Hendi Muhammad
METIK JURNAL (AKREDITASI SINTA 3) Vol. 7 No. 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.591

This research focuses on analyzing the impact of social media on society, particularly addressing the issue of cyberbullying on the Twitter platform. Based on statistics, the majority of internet users in Indonesia actively utilize social networks, with Twitter being the most dominant platform used for communication and interaction. Therefore, cyberbullying cases often occur on this social media platform. In this study, two classification methods, namely Decision Tree and K-Nearest Neighbor (KNN), were employed to classify cyberbullying-related messages on Twitter. The aim of this research is to compare the performance of these two methods and to identify early signs of cyberbullying as relevant digital evidence for legal proceedings. The dataset used in this study consists of 650 comment records from the period 2019 to 2021, with predefined labels. The analysis results indicate that K-Nearest Neighbor achieved the highest accuracy, reaching 75.99%, compared to Decision Tree with 65.00%. Hence, K-Nearest Neighbor is considered a more effective method for cyberbullying analysis on the Twitter platform. Additionally, the identification of early signs of cyberbullying in comment id 2 can serve as relevant digital evidence for legal purposes. This research provides better insights into the effectiveness of classification in addressing cyberbullying issues on the Twitter platform.

Klasifikasi Gambar Bahasa Isyarat Indonesia (Bisindo) Pada Komunitas Tuli Menggunakan Machine Learning Candra, Kurnia Khoirul; Kusrini, Kusrini
e-Jurnal JUSITI (Jurnal Sistem Informasi dan Teknologi Informasi) Vol. 14 No. 1 (2025): e-Jurnal JUSITI
Publisher : Universitas Dipa Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36774/jusiti.v14i1.1649

Bahasa isyarat merupakan sebuah cara komunikasi khusus yang digunakan para penyandang disabilitas khususnya penyandang Tuli. Pada sebuah sistem pengembangan komunikasi di masa kini bahasa utama yang digunakan komunitas Tuli di Indonesia adalah Bahasa Isyarat Indonesia (BISINDO). Tujuan penelitian ini yaitu mengimplementasikan metode klasifikasi gambar BISINDO menggunakan metode machine learning, mengetahui akurasi penerapan gambar BISINDO dengan menggunakan machine learning. Jenis penelitian ini adalah penelitian kualitatif, klasifikasi gambar Bahasa Isyarat Indonesia (BISINDO) menggunakan teknik machine learning. Metode ini digunakan untuk menggunakan model Convolutional Neural Networks (CNN) yang tersedia pada dataset gambar dapat meningkatkan akurasi. Hasil pada penelitian yang dilakukan penulis menunjukkan bahwa metode CNN memiliki akurasi 98,07% dan metode machine learning memiliki akurasi 100%. Kesimpulan menunjukkan implementasi metode Convolutional Neural Network (CNN) dengan menggunakan arsitektur SSD MobileNet dan machine learning dalam melakukan deteksi objek pada simbol abjad Bahasa Isyarat Indonesia (BISINDO) berjalan dengan baik dan menghasilkan keberhasilan pendeteksian simbol abjad BISINDO dengan peningkatan akurasi yang baik.

Sistem Pendukung Keputusan Pemberian Tugas Belajar Pegawai Negeri Sipil Tukan, Ewaldus Ambrosius; Kusrini, Kusrini; Al Fatta, Hanif
Jurnal Informa : Jurnal Penelitian dan Pengabdian Masyarakat Vol 9 No 1 (2023): Juni
Publisher : Politeknik Indonusa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46808/informa.v9i1.244

The awarding of study assignment scholarships for civil servants is carried out to improve professional competence and professional skills, taking into account several aspects, namely (1) Service Period, (2) Age, (3) Achievement, (4) Performance, (5) Discipline Knowledge, and (6) Good Behavior, which was initially used in the scholarship selection process for study assignments without calculation.Utilization of a decision support system was chosen as a solution to overcome problems in selecting scholarships for study assignments for civil servants within the scope of local government at the district level so as to minimize the occurrence of errors in giving these learning assignments to civil servants using the Multifactor Evaluation Process (MFEP) method. By making multi-factor decisions, subjective and intuitive decision making can be avoided.The results of the study obtained an alternative with the highest total evaluation factor weight at 14.95 and it was concluded that the calculation of the selection of scholarships for study assignments using a decision support system obtained results that could be accounted for in supporting the decision making carried out by the regional head at the district level concerned.

Co-Authors AA Sudharmawan, AA Abdillah, Yahya Auliya Abdullah Sukri, M Iqbal Abdullah, Mochamad Fadillah Achmad Oddy Widyantoro Ade Pujianto, Ade Adhani, Muhammad Azmi Agastya, I Made Artha agung budi AGUS PURWANTO Ahmad Yusuf Aji Santoso, Bayu Aji Susanto Anom Purnomo Alfatta, Hanif Alva Hendi Muhammad Andi Muhammad Irfan Andi Sunyoto Andika, Roy Andriyanto, Rifki Angga Kurniawan Anggit Dwi Hartanto, Anggit Dwi Anggraeni, Meita Dwi Ardana, Wildan Muhammad Ardana, Wildan Muhammmad Ardiansyah, Fachri Ari Yuana, Kumara Arief Setyanto Arief, M Rudyanto Arief, Muhammad Rudyanto Arifuddin, Danang Arik Sofan Tohir Aris Subadi Arli Aditya Parikesit Asnawi, Muhamad Fuat Atin Hasanah Azi, Amanda Aziz Muzani, Ma'ruf Aziz, Moh Abdul Azkar, Azkar Bayu Setiaji Béjar, Rodrigo Martínez Bentar Candra P Bernadhed, Bernadhed Bisono, Hadi Hikmadyo Braeken, An Buana, Yopy Tri Candra, Kurnia Khoirul da Silva, Bruno Darmawan, Eko Rahmad David Agustriawan DHANI ARIATMANTO Dzulhijjah, Dwi Ahmad Eko Pramono Eko Purwanto Ema Utami Emha Taufiq Luthfi Fatkhurrochman, Fatkhurrochman Fauzi, Moch Farid Fauzy, Marwan Noor Febrianti, Winda Febriyanti, Nada Rizki Ferry Wahyu Wibowo fitriyanto, nur Gifari, Okta Ihza Halimi, Ahmad Hamdikatama, Bimantyoso Hanafi Hanafi Hanif Al Fatta Hari Muktafin, Elik Haris, Ruby hartanto, david budi Hartono, Anggit Dwi Haryo, Wasis Hasan, Nur Fitrianingsih Hasan, Nurul Rahmawati Helmawati, Nita Herawati, Maimi Heri Abijono, Heri Herlinawati, Noor Hulvi, Alfajri I Putu Agus Ari Mahendra Ikhwanudin, Aolia Ilmawati, Fahma Inti Jeki Kuswanto Juwariyah, Siti Kasman, Haris Saktiawan Kurniasari, Iin Kusnawi , Kusnawi Kusnawi Kusnawi Lewu, Retzi Y. Linda, Kumara Dewi Listyanto, Ahmad Wildan López, Alba Puelles Lukman Bachtiar M. RUDYANTO ARIEF M. Suyanto, M. Madhika, Yudha Randa Mahendra, Awanda Putra Mangun, Syamsul Syahab Maradona, Maradona Mardiana Mardiana Martínez-Béjar, Rodrigo Masruri, Nizar Haris Masud, Ibnu maulana, fahrizal Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Metha, Halifa Sekar Miftachuddin, Achmad Agus Athok Mohamad Firdaus, Mohamad Mohammad Diqi Mohammad Rezza Pahlevi Moningka, Nirwan Mufti Ari Bianto Muhamad Iksan, Muhamad Muhammad Resa Arif Yudianto Muktafin, Elik Hari Mulia Sulistiyono Muzakir, Muhammad MZ, Reza Rafiq Nasiri, Asro Ngaeni, Nurus Sarifatul Ni Nyoman Utami Januhari, Ni Nyoman Nugroho, Agung Nugroho, Hanantyo Sri Nuk Ghurroh Setyoningrum Nurmalasari, Maulidya Dwi Oktafiqurahman, Andi Olajuwon, Sayyid Muh. Raziq Onde, Mitrakasih La ode Oscar Samaratungga Pamoengkas, Muhamad Agoeng Pamungkas, Sapto Pradipta, Dody Prameswari, Sonia Anjani Prasetio, Agung Budi Prastyo, Rahmat Pratama, Muhammad Egy Puri, Fiyas Mahananing Purnamasari, Resti Putra, Andriyan Dwi Rachmawati Oktaria Mardiyanto RAMADHAN, SYAIFUL Rasyid, Magfirah Raynald Alfian Yudisetyanto Riduan, Nor Rizkayati, Anisa S, Muhamad Rois S, Muhammad Sabri Saleh, Robby Febrianur Samponu, Yohakim Benedictus Santosa, Hendriansyah SANTRI SANTRI Saputro, Moh. Rizal Bayu Sarawan, Tommy Sari, Yayak Kartika Selvy Megira, Selvy Semma, Andi Bahtiar Sentoso, Thedjo Setiawan, Moh. Arif Ma'ruf Setyanto, Arif Siswo Utomo, Mardi Slamet . Solikin, Arif Fajar Sudarmawan, Sudarmawan Sudarto Sudarto Swastikawati, Claudia Syafutra, Arif Dwi Syaiful Huda Tala, WD. Syarni Tampubolon, Jandri Tamuntuan, Virginia Toifur, Tubagus TONNY HIDAYAT Tri Nugroho, Arief triadin, Yusrinnatul Jinana Tukan, Ewaldus Ambrosius Ula, M. Izul Wahyu Pujiharto, Eka Wahyudi, Alfian Cahyo Wangsa, Sabda Sastra Wijaya, Jodi Wiwi Widayani, Wiwi Yanuargi, Bayu Yossy Ariyanto Yuana, Kumara Ari Yuza, Adela Zakaria Zakaria Zuhri, Muhammad Rafli Zulkarnain, Imam Alfath Zumarni, Zumarni

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