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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Semantik Jurnal Simetris Jurnal Transformatika Seminar Nasional Informatika (SEMNASIF) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika International Journal of Artificial Intelligence Research Tekinfo | Scientific Journal of Industrial and Information Engineering Applied Industrial Engineering Journal Elektrika Indonesia Journal of Halal IJID (International Journal on Informatics for Development) Repertorium: Jurnal Ilmiah Hukum Kenotariatan Journal of Electronics, Electromedical Engineering, and Medical Informatics Abdimasku : Jurnal Pengabdian Masyarakat JTEIN: Jurnal Teknik Elektro Indonesia Jurnal Locus Penelitian dan Pengabdian
Sari Ayu Wulandari
Universitas Dian Nuswantoro
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Perancangan Sistem Monitoring dan Kontrol Produksi Garam Berbasis Internet of Things Wulandari, Sari Ayu; Ramadhan, Rifky Daffa; Firdausy, Farah Mutia; Maghfi, Risya Ulayyar; Setyawan, Yoga; Paryanto, Paryanto; Muslim, Zulkifli Alif
Indonesia Journal of Halal Vol 4, No 2 (2021): IJH
Publisher : Pusat Kajian Halal Undip

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/halal.v4i2.13687

Abstract

Abstrak Garam merupakan salah satu kebutuhan dan komoditas terpenting dalam kehidupan sehingga penyediaan, pengadaan dan distribusi garam menjadi sangat penting. Studi ini bertujuan untuk merancang sistem monitoring dan kontrol berbasis IoT pada pengukuran kadar garam dan suhu serta kelembapan tempat penyimpanan guna menciptakan proses produksi dan distribusi yang efisien. Uji ketepatan sensor kadar garam terhadap hasil titrasi kadar garam metode argentometri didapatkan hasil rata-rata error 5,00%. Hal ini menunjukkan bahwa sistem yang dirancang masih membutuhkan perningkatan. Sementara itu, hasil monitoring suhu dan kelembapan yang telah dilakukan menunjukkan bahwa suhu dan kelembapan berada pada suhu rata-rata 28,9°C dan kelembapan rata-rata sebesar 68,2%. Hal tersebut mengindikasikan kondisi penyimpanan cukup optimal sehingga dapat meminimalisir penurunan kadar yodium. Kata kunci: Garam, IoT, kadar, monitoring, suhu dan kelembapan  AbstrakDESIGN OF SALT PRODUCTION MONITORING AND CONTROL SYSTEM BASED ON THE INTERNET OF THINGS. Salt is one of the most essential necessities and commodities in life, hence the supply, procurement, and distribution of salt is important. This study aims to design an IoT-based monitoring and control system for measuring salt content (purity) and temperature and humidity in storage areas to create an efficient production and distribution process. The accuracy test of the salt purity sensor compared to the results of argentometric titration showed an average error of 5.00%. This showed that the designed system still needed improvement. Meanwhile, the results from temperature and humidity monitoring showed an average of 28.9 oC and 68.2% respectively, indicating sufficient conditions to minimize iodine content reduction.56Keywords: IoT, monitoring, salt, salt purity, temperature and humidity
Deep learning for audio signal-based tempo classification scenarios Muljono, Muljono; Nurtantio Andono, Pulung; Ayu Wulandari, Sari; Al Azies, Harun; Naufal, Muhammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1687-1701

Abstract

This article explains how to determine the tempo of the kendhang, an Indonesian traditional melodic instrument. This research presents novelty as technological research related to gamelan instruments, which has rarely been achieved thus far, through the introduction of kendhang tempo types through the sounds produced, with the hope of creating an automatic system that can recognize the kendhang tempo during a gamelan performance. The testing in this work will categorize the tempo of kendhang into three categories: slow, medium, and fast, utilizing one of the two scenario models proposed, mel frequency cepstral coefficients (MFCC) and convolutional neural network (CNN) in the first scenario, and mel spectrogram and CNN in the second. Kendhang's original audio data, which was captured in real time and later enhanced, makes up the data set. The model 1 scenario, which entails feature extraction using MFCC and classification using the CNN classification approach, is the best scenario in this research, based on the experimental results. When compared to the other suggested modeling scenarios, model 1 has a level of 97%, an average accuracy, and a gain value of 96.67%, making it a solid assistant in terms of kendhang's good tempo recognition accuracy.
Exploiting Silhouette Principle Component For Dimension Reduction In Breast Ultrasound Images Classification Kartikadarma, Etika; Fanani, Ahmad Zainul; Pujiono, Pujiono; Affandy, Affandy; Wulandari, Sari Ayu
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1165

Abstract

This paper proposes the use of the Dimensional Reduction method with the Silhouette Principle Component (SPC) Approach to improve the classification of breast ultrasound images. The PCA method is used to reduce the dimensions of medical images to improve classification, with MobileNet-v2 and DenseNet-121 as the optimal classification algorithm choices. The results show that the SPC method succeeded in producing efficient feature representation with data sizes that are almost the same as the original data, while PCA produces greater dimensionality reduction. The SPC model also shows the best performance in terms of accuracy and loss. This research makes a significant contribution to the development of more sophisticated and efficient medical image analysis techniques to support the diagnosis and treatment of breast cancer.
Klasifikasi Jenis Kulit Wajah menggunakan Backpropagation Neural Networks Berbasis GLCM HERYANTO, M. ARY; JUANANTA, DENY; SADANARESWARI, AGATA; WULANDARI, SARI AYU
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 3: Published July 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i3.705

Abstract

ABSTRAKKulit wajah sangat sensitif dibandingkan dengan bagian tubuh lainnya. Ada beberapa jenis kulit wajah, yaitu normal, berminyak, dan kering. Namun, terkadang mengidentifikasi jenis kulit wajah seseorang dengan benar bisa menjadi masalah karena terdapat lima jenis kulit wajah yang berbeda. Untuk mengatasi kesulitan dalam mengidentifikasi jenis kulit wajah, pada penelitian ini diterapkan metode klasifikasi Backpropagation Neural Network berbasis GLCM. Penelitian ini menggunakan tiga jenis kulit wajah, yaitu: kering, berminyak, dan normal. Sedangkan untuk mencari model arsitektur yang tepat dilakukan dengan cara variasi jumlah hidden layer dan jumlah neuron per hidden layer. Setelah dilakukan beberapa pengujian didapatkan hasil akurasi 96.70% untuk model sembilan lapisan tersembunyi dengan enam neuron pada tiap lapisan tersembunyi.Kata kunci: kulit wajah, klasifikasi, backpropagation neural network, GLCM. ABSTRACTFacial skin is very sensitive compared to other body parts. There are several facial skin types: normal, oily, and dry. However, sometimes correctly identifying a person's facial skin type can be problematic because there are five different skin types. To overcome difficulties in identifying facial skin types, this study applied the GLCM-based Backpropagation Neural Networks classification method. This study used three types of facial skin, namely: dry, oily, and normal. Meanwhile, finding the right architectural model is done by varying the number of hidden layers and the number of neurons per hidden layer. After several tests, the results obtained an accuracy of 96.70% for the nine hidden layers model with six neurons for each hidden layer.Keywords: facial skin, classification, backpropagation neural network, GLCM.
Analisis Sinyal Jantung Menggunakan Metode Fuzzy C-Means (FCM) Clustering untuk Deteksi Aritmia Nadlirah, Tasnim Ahya; Aripin, Aripin; Wulandari, Sari Ayu
Applied Industrial Engineering Journal Vol. 5 No. 2 (2021): DESEMBER
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/aiej.v5i2.8953

Abstract

Aritmia jantung adalah salah satu klasifikasi penyakit kardiovaskuler yang menjadi penyebab kematian terbanyak. Seseorang yang telah sembuh dari sakit jantung akan mudah untuk terserang kembali. Sehingga mereka harus mendapatkan alat pemantauan jantung seperti EKG (Elektrokardiogram). Sistem komputer dengan machine learning dapat digunakan untuk membantu membaca hasil rekaman EKG. Namun, kendala untuk mewujudkan identifikasi otomatis menggunakan Sistem komputer merupakan sebuah peranti elektronik yang menghasilkan sejumlah besar data digital melalui pemanfaatan EKG. Ini terjadi karena elektrokardiogram merekam aktivitas listrik jantung dalam satuan milivolt (mV) setiap beberapa detik. Salah satu algoritma yang dapat digunakan adalah Fuzzy C-Means clustering (FCM). Oleh karena itu, kami melakukan penelitian untuk mendeteksi aritmia menggunakan algoritma pengelompokan Fuzzy C-Means (FCM). Informasi yang kami gunakan dalam penelitian ini berasal dari sensor ECG 3-lead AD8232 untuk sampel yang normal dan dari database Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) untuk sampel yang terjangkit di kantong. Hasil pengujian FCM 3D menunjukkan akurasi rata-rata 65% dengan menggunakan 200 titik data per pengujian.
Klasifikasi Saturasi Oksigen dengan Pulse Oximetry Menggunakan Metode Fuzzy Subtractive Clustering (FSC) Nurdin, Muhammad Rizal; Prasetyanto, Wisnu Adi; Wulandari, Sari Ayu
Applied Industrial Engineering Journal Vol. 5 No. 2 (2021): DESEMBER
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/aiej.v5i2.8956

Abstract

Saat memeriksa kondisi pasien di rumah sakit, banyak parameter yang perlu diukur, salah satunya adalah SpO2. Tes SpO2 adalah suatu metode yang dipakai untuk menghitung jumlah oksigen dalam darah. Kisaran nilai SpO2 yang umum adalah 85-100%. Pengklasifikasian kadar oksigen darah dilakukan dengan melakukan metode pengelompokan, salah satunya adalah pengelompokan pengurangan fuzzy, menggunakan data yang diperoleh dari berbagai kondisi pasien. Pengklasifikasi oksigen darah (SpO2) ini menggunakan metode pengumpulan data dengan mengambil data normal dan hipoksia untuk tujuan membandingkan kadar oksigen darah. Saturasi oksigen darah yang diperoleh dikenai dengan metode Fuzzy Subtractive Clustering yang dihasilkan berupa densitas dengan titik-titik dalam suatu ruang (variabel). Sofware yang digunakan yaitu MATLAB sebagai platform perbandingan dengan versi MATLAB R2015a. Pada pengujian penelitian ini pengenalan pola sinyal SpO2 normal dan hipoksemia menggunakan Fuzzy Subtractive Clustering mempunyai akurasi 93,33 %.
Feature Selection Method to Improve the Accuracy of Diabetes Mellitus Detection Instrument Wulandari, Sari Ayu; Madnasri, Sutikno; Pramitasari, Ratih; Susilo, Susilo
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09203

Abstract

The need for aroma recognition devices or often known as enose (electronic nose), is increasing. In the health field, enose can detect early diabetes mellitus (DM) type 2 from the aroma of urine. Enose is an aroma recognition tool that uses a pattern recognition algorithm to recognize the urine aroma of diabetics based on input signals from an array of gas sensors. The need for portable enose devices is increasing due to the increasing need for real-time needs. Enose devices have an enormous impact on the choice of the gas sensor Array in the enose. This article discusses the effect of the number of sensor arrays used on the recognition results. Enose uses a maximum of 4 sensors, with a maximum feature matrix. After that, the feature matrix enters the PCA (Principal Component Analysis) feature extraction and clustering using the FCM (Fuzzy C Means) method. The number of sensors indicates the number of features. Enose using method for feature selection, it’s a variation from 4 sensors, where experiment 1 uses 4 sensors, experiment 2 uses a variation of 3 sensors and experiment 3 uses a variation of 2 sensors. Especially for sensors 3 and 4 using feature extraction method, PCA (Principal Component Analysis), to reduce features to only 2 best features. As for the variation of 2 sensors use primer feature matrix. After feature selection, the number of features is 2 out of 11 variations. Next, do the grouping using the FCM (Fuzzy C Means) method. The results show that using two sensors has a high accuracy rate of 92.5%.
Pengelompokan Jenis Kulit Normal, Berminyak dan Kering Menggunakan 4-Connectivity dan 8-Connectivity Region Properties Berdasarkan Ciri Rerata Bound Wulandari, Sari Ayu
Jurnal Transformatika Vol. 17 No. 1 (2019): July 2019
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v17i1.1341

Abstract

Produk kosmetik, sabun dan perawatan kulit wajah, mengkhususkan penggunaan pada beberapa jenis kulit. Ada 5 jenis kulit wajah yaitu normal, berminyak, kering, sensitif dan kombinasi. Mengetahui jenis kulit wajah sangat penting untuk memilih produk kosmetik yang cocok. Diperkirakan sekitar 1518 individu dengan kasus penyakit kulit baru dilaporkan di tahun 2018. Dalam makalah ini, dilakukan proses deteksi jenis kulit wajah dengan menggunakan metode 4 dan 8 konektifitas. Tujuan dari makalah ini adalah untuk melakukan deteksi jenis kulit wajah serta merancang bangun sebuah teknologi pengolahan citra digital yang dapat mengenali jenis kulit wajah. Metode yang digunakan meliputi akuisisi citra, peningkatan kualitas citra, segmentasi citra, ekstraksi ciri dan pengenalan pola. Manfaat dari makalah ini adalah dapat mengetahui jenis kulit wajah, sehingga dapat memberikan perlakuan yang tepat, sehingga tingkat kesalahan dalam memilih kosmetik dapat diminimalisir.
PID Tuning for Robustness to Noise in DC Motor Angular Position Control System Pambudi, Arga Dwi; Heryanto, M Ary; Wulandari, Sari Ayu
Jurnal Teknik Elektro Indonesia Vol 6 No 2 (2025): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v6i2.751

Abstract

DC motor position control often encounters challenges related to noise interference, which can degrade system performance in industrial applications. This study evaluates the performance of PID controllers using tuning approaches based on Ziegler-Nichols and Cohen-Coon methods and develops an iterative tuning method based on system response evaluation to enhance robustness. Simulations were conducted in MATLAB/Simulink, integrating White Gaussian Noise and Sinusoidal Noise to test the system's resilience. Initial results showed that Ziegler-Nichols achieved the fastest rise time (0.006 s) but with a high overshoot (61.78%). Meanwhile, the Cohen-Coon method demonstrated lower overshoot (32.49%) but became unstable under noisy conditions. To address these weaknesses, parameter refinement for Kp, Ki, and Kd was performed using a trial-and-error approach. Final results indicated that the combination of Kp=25, Ki=100, and Kd=0.6 reduced the overshoot to 4.74%, settling time to 0.759 s, and maintained a low steady-state error (1.5%). This study highlights that the trial-and-error approach can enhance system robustness against noise while providing a practical solution for DC motor control systems in real-world applications.
Peningkatan Akurasi Klasifikasi Awan Cumulonimbus Dari Satelit Himawari-8 Saat Cuaca Ekstrem Dengan Menggunakan Metode Grayscale Thermal Image Dan Neural Network Backpropagation Triyotomo, Triyotomo; Wulandari, Sari Ayu; Soeleman, M. Arief; Zami , Farrikh Al
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 10 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i10.4915

Abstract

Cumulonimbus is the only type of cloud that can produce hail, lightning, and thunder. This type of cloud can cause extreme weather that causes damage to public infrastructure and can also cost lives. This research aims to improve Cumulonimbus cloud detection on the Himawari-8 satellite using a combination of the Grayscale Thermal Image method and the method of Artificial Neural Network Backpropagation. The data was taken during the transition season, which is a potential time the onset of extreme weather caused by Cumulonimbus clouds is quite large, and the consequences incurred can cause very significant losses. To detect Cumulonimbus, The Himawari-8 Satellite Image is pre-processed so that an image is obtained gray thermal, then the image is converted into digital data in the form of numbers and from the characterization of the results using histograms. The last process is classified using Artificial Neural Network Propagation. All processes in this study use Matlab to obtain the best classification accuracy. The expected result is an increase in the value of accuracy when using the method of grayscale thermal image compared without using this method. Each accuracy value training data, validating data, and testing data obtained increased from 96.6%, 84.46%, and 80.02 to 100%, 88.9%, and 91.7%.
Co-Authors Achmad Syarifuddin Affandy Affandy Ahmad Zainul Fanani Arga Dwi Pambudi Aripin Arsita, Cynthia Badia, Giulia Salzano Dedi Nurcipto, Dedi Dian Retno Sawitri Dita Ayu Mayasari, Dita Ayu Dwi Nurul Izzhati Erini, Widya Surya Etika Kartikadarma Firdausy, Farah Mutia Harun Al Azies Hasan Mastrisiswadi Heryanto, M Ary I Ketut Swakarma JUANANTA, DENY Kurniatie, Menik Dwi Kusmiyati Kusmiyati Listianingrum, Nia Yunita M Ary Heryanto Madnasri, Sutikno Maghfi, Risya Ulayyar Menik Dwi Kurniatie Muhammad Naufal, Muhammad Muljono Muljono Muslim, Zulkifli Alif Nadlirah, Tasnim Ahya Nurdin, Muhammad Rizal Paryanto Pramitasari, Ratih Pujiono Pujiono Pulung Nurtantio Andono Purnomo, Muhammad Agus Rachelina Nur Ilyasaf Rahadian, Arief Raharjo, Sofyan Budi Ramadhan, Rifky Daffa rexza berliyana Ridwan Ridwan Rudy Tjahyono Rudy Tjahyono, Rudy SADANARESWARI, AGATA Sari Wijayanti Sari Wijayanti Setiadi, Kristoforus Ardian SETYAWAN, YOGA Soeleman, M. Arief Susilo Susilo Thomas, Gracia Putri Tita Talitha Tjahyono, Rudi Triyotomo, Triyotomo Wisnu Adi Prasetyanto Zaenal Arifin Zami , Farrikh Al