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Performance Comparison of Firefly and Cuckoo Search Algorithms in Optimal Thresholding of Cancer Cell Images Wonohadidjojo, Daniel Martomanggolo
ComTech: Computer, Mathematics and Engineering Applications Vol 10, No 1 (2019): ComTech (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v10i1.5632

This research presented a performance comparison of the two methods in cancer cells image processing. Each method consisted of two stages. The first stage was image enhancement using fuzzy sets. The second stage was optimal fuzzy entropy based image thresholding. In the thresholding stage, the first method used Firefly Algorithm (FA) and the second used Cuckoo Search (CS). In both methods, four performance metrics (Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structured Similarity Indexing Method (SSIM), and Feature Similarity Indexing Method (FSIM)) and variance and entropy of the images were computed to validate the comparison. The image histograms of both methods show that the distribution of red, green, and blue channel is better than the histograms of original images. In terms of the four metrics, the method that uses FA shows higher performance than CS. In terms of image variance and entropy, the method using CS shows better results than FA. These results suggest that when the performance metrics used are MSE, PSNR, MSSIM, and FSIM, the method using FA is more suitable for cancer cells image enhancement and thresholding. However, when the variance and entropy of the images are used as the performance metrics, the method using CS is more suitable for cancer cells image enhancement and thresholding. Both methods will be useful to assist in the analysis of cancer cell images by the experts in the field.

WORKSHOP MEMBUAT WEBSITE BERBASIS PLATFORM WORDPRESS.COM Dinata, Yuwono Marta; Lestari, Caecilia Citra; Wonohadidjojo, Daniel Martomanggolo; Tjahjono, Laura Mahendratta; Engel, Mychael Maoeretz
Jurnal LeECOM (Leverage, Engagement, Empowerment of Community) Vol 2 No 2 (2020): Jurnal LeECOM
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37715/leecom.v2i2.1590

Pelatihan workshop yang dilakukan oleh Fakultas Teknologi Informasi Program Studi Informatika Universitas Ciputra bertujuan untuk memperluas wawasan para fasilitator Yayasan Kaki Dian Emas (YKDE) dalam literasi digital. YKDE dipilih berdasarkan dari interview melalui WhatsApp messenger maupun menelpon langsung. Hal karena pelatihan ini dilakukan pada masa pandemic Covid-19. Pelatihan ini diperuntukkan untuk fasilitator YKDE yang tersebar di seluruh Indonesia. Kebutuhan media untuk berbagai cerita atau pengalaman menjadi hal yang penting. Melalui kegiatan ini, para fasilitator menjadi lebih terbuka wawasannya untuk menggunakan media website online. Setelah mengikuti kegiatan tersebut, mereka bisa langsung menerapkan dan membuat website untuk kepentingan berbagai informasi sehingga fasilitator di seluruh Indonesia bisa saling berbagi informasi secara real time.

Workshop Computational Thinking untuk Guru SD, SMP dan SMA oleh Biro Bebras Universitas Ciputra Surabaya Wonohadidjojo, Daniel Martomanggolo; Citra, Caecilia Citra; Tanamal, Rinabi; Soekamto, Yosua Setyawan; Maryati, Indra
Jurnal LeECOM (Leverage, Engagement, Empowerment of Community) Vol. 3 No. 2 (2021): Jurnal LeECOM
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37715/leecom.v3i2.2268

Pada periode tahun 2000 hingga 2018 dapat diketahui bahwa meskipun terjadi kenaikan pada nilai Programme for International Student Assesment untuk Indonesia tetapi nilai Indonesia relatif turun pada semua bidang. Cara berpikir Computational Thinking (CT) menggunakan pendekatan pemrosesan informasi dengan cara penalaran, pemikiran procedural dan algoritmik serta rekursif. Diharapkan dengan naiknya kemampuan analitik yang mendasari (CT), rating Indonesia untuk tes internasional seperti test PISA dapat meningkat. Bebras Indonesia adalah suatu komunitas CT yang didirikan di Indonesia. Pada bulan April 2021, Biro Bebras Universitas Ciputra Surabaya dalam Gerakan PANDAI dengan dukungan Google.org memberikan workshop kepada para guru SD, SMP dan SMA. Metode yang digunakan pada workshop ini terdiri dari 3 tahap yaitu penjelasan konsep dan materi CT serta resources yang digunakan, penjelasan aplikasi CT pada kehidupan sehari-hari, dilanjutkan dengan penjelasan dan latihan untuk menerapkan CT pada matapelajaran tiap peserta dalam konteks micro teaching, dan terakhir adalah peserta menjalankan tugas menerapkan pada matapelajaran di sekolah masing-masing dalam konteks Micro Teaching dan melaporkan hasilnya. Berdasarkan survey yang dilaksanakan didapatkan bahwa pada 3 hari workshop peserta menyatakan setuju dan sangat setuju bahwa topik pada worksjop tersebut berkaitan dengan pekerjaan mereka. Pada tahap Micro Teaching para peserta berhasil menerapkan CT pada matapelajaran di sekolah masing-masing. Para peserta telah berhasil mengumpulkan tugas-tugas yang diberikan yang menggambarkan bahwa workshop ini telah berjalan dengan baik dan menunjukkan keberhasilan guru-guru dalam menerapkan CT pada matapelajaran di sekolah-sekolah masing-masing.

Performance Comparison of Firefly and Cuckoo Search Algorithms in Optimal Thresholding of Cancer Cell Images Daniel Martomanggolo Wonohadidjojo
ComTech: Computer, Mathematics and Engineering Applications Vol. 10 No. 1 (2019): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v10i1.5632

This research presented a performance comparison of the two methods in cancer cells image processing. Each method consisted of two stages. The first stage was image enhancement using fuzzy sets. The second stage was optimal fuzzy entropy based image thresholding. In the thresholding stage, the first method used Firefly Algorithm (FA) and the second used Cuckoo Search (CS). In both methods, four performance metrics (Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structured Similarity Indexing Method (SSIM), and Feature Similarity Indexing Method (FSIM)) and variance and entropy of the images were computed to validate the comparison. The image histograms of both methods show that the distribution of red, green, and blue channel is better than the histograms of original images. In terms of the four metrics, the method that uses FA shows higher performance than CS. In terms of image variance and entropy, the method using CS shows better results than FA. These results suggest that when the performance metrics used are MSE, PSNR, MSSIM, and FSIM, the method using FA is more suitable for cancer cells image enhancement and thresholding. However, when the variance and entropy of the images are used as the performance metrics, the method using CS is more suitable for cancer cells image enhancement and thresholding. Both methods will be useful to assist in the analysis of cancer cell images by the experts in the field.

Perbandingan Performa Histogram Equalization untuk Peningkatan Kualitas Gambar Minim Cahaya pada Android Claudia Kenyta; Daniel Martomanggolo Wonohadidjojo
Ultimatics : Jurnal Teknik Informatika Vol 12 No 2 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v12i2.1667

When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Equalization (CLAHE). This paper shows the implementation of HE algorithm and its performance in enhancing the quality of photos taken in low light condition on Android based application and the comparison with CLAHE algorithm. The results show that, HE algorithm is better than CLAHE algorithm.

Perbandingan Convolutional Neural Network pada Transfer Learning Method untuk Mengklasifikasikan Sel Darah Putih Daniel Martomanggolo Wonohadidjojo
Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i1.2040

Analysis of WBC structure from microscopic images and classification of cells into types is challenging. Although white blood cells can be differentiated based on their shape, color and size, one challenging aspect is that they are surrounded by other blood components such as red blood cells and platelets. In this study, transfer learning method using four network architectures that have been trained in advance is applied to classify the white blood cell images. The network architectures used are AlexNet, GoogleNet, ResNet-50 and VGG-16. A comparative analysis of the performance of these architectures was carried out in classifying the images. The evaluation method was undertaken using Confusion Matrix. The performance metrics measured in the evaluation are Accuracy, Precision, Recall and Fmeasure. The results showed that all architectures succeeded in classifying white blood cells using the transfer learning method. ResNet-50 is the network architecture that shows the highest performance in classifying white blood cell images.

Sistem Kendali Jarak Jauh untuk Smart Home Melalui Aplikasi Android Menggunakan NodeMCU dan Firebase Daniel Martomanggolo Wonohadidjojo; Hansel Santoso
Poltanesa Vol 23 No 1 (2022): Juni 2022
Publisher : P2M Politeknik Pertanian Negeri Samarinda

Smart home merupakan rumah dengan beberapa perangkat pintar yang terpasang di dalam rumah. Perangkat pintar ini merupakan bagian dari Internet of Things (IoT), dengan tujuan untuk membantu pemilik rumah dalam memantau dan mengendalikan keadaan dalam rumah. Perangkat pintar dapat dioperasikan oleh pemilik rumah di dalam maupun di luar rumah dengan menggunakan cloud storage. Dalam penelitian ini hal tersebut diimplementasikan dengan perangkat keras NodeMCU. Setiap sensor yang terhubung dengan NodeMCU mengirimkan data ke pusat untuk dikirimkan ke cloud storage yang bernama Firebase menggunakan metode WebSocket. Data yang disimpan di Firebase akan dikirim ke aplikasi Android yang dibangun menggunakan Flutter. Dengan demikian sistem ini mampu membantu pemilik rumah untuk memantau dan mengendalikan perangkat elektronik di dalam rumah menggunakan aplikasi Android dimana Aplikasi ini dihubungkan dengan NodeMCU menggunakan cloud real-time database.

Evaluasi Performa Panel Surya Terintegrasi Bangunan berdasarkan Standar Greenship: Menuju Bangunan Sekolah Net Zero Energi Susan Susan; Dyah Kusuma Wardhani; Yusuf Ariyanto; Daniel Martomanggolo Wonohadidjojo; Eric Harianto
EMARA: Indonesian Journal of Architecture Vol. 8 No. 1 (2022): Vol. 8 No. 1 (2022): EIJA August-October edition
Publisher : Universitas Islam Negeri Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/eija.v8i1.1442

generated from a variety of sources, both renewable and nonrenewable. Switching from nonrenewable to renewable energy sources is one of many strategies that can be used to achieve net-zero buildings. In Indonesia, this strategy is very feasible due to its abundant renewable energy resources, particularly solar energy. This research presents a school building as the proposed case. The school, SCK Citra Garden, is chosen as the pilot project due to its access to solar radiation and its minimum shading conditions. Using Helioscope software, BIPV modelling was simulated on its roof, and the electrical energy output from BIPV was calculated. The substitution percentages of BIPV energy output for conventional electrical energy consumed by the building were then measured. This percentage was compared to the National Energy Mix target and Greenhouse Gas Standard to assess its performance towards net-zero school buildings. The result shows that BIPV has a good performance. Even though the substitution percentage is still below the national energy mix target, it exceeds the greenhouse gas standard target for on-site renewable energy tools.

Classification of Bacterial Images using Transfer Learning, Optimized Training and Resnet-50 Daniel Martomanggolo Wonohadidjojo
Eduvest - Journal of Universal Studies Vol. 2 No. 2 (2022): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Bacterial image analysis using traditional laboratory methods encounters bacterial recognition errors and requires extra experience and long processing time. Therefore, the automated classification technique of bacterial images is more useful than traditional visual observations for biologists because of their accurate classification, low cost, and fast diagnosis. In this study, a method to classify bacteria images by implementing the CNN deep learning method using Transfer Learning is proposed. This trained ResNet-50 is implemented as the CNN architecture. In the training of the classification layer, SGDM optimizer is used. The classification performance for is evaluated in using confusion matrix and four performance metrics: Accuracy, Precision, Recall and Fmeasure. The Confusion Matrix and all the performance metrics show is successful in classifying bacterial images.

Penerapan Algoritma Yolov4-Tiny Dan Efficientnetv2-S Untuk Deteksi Kesegaran Ikan Gurami Hans Richard Alim Natadjaja; Daniel Martomanggolo Wonohadidjojo
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i2.633

Fish production is one of the largest in Indonesia. Among many fishery products, Gurami fish is one of the most widely processed by society. However, the decrease in the freshness of fishery products is susceptible to occur when they reach the hands of consumers. Several methods for detecting fish freshness have been applied to assist in obtaining fresh fish products. However, some of these methods have a lack of accuracy rate, not all of them were developed to detect gurami freshness. Therefore, a solution is needed to help people detect the freshness of Gurami fish and make it accessible on mobile devices. This solution can be realized by using Deep Learning methods. The methods proposed in this study use the Convolutional Neural Network (CNN) by utilizing the YOLOv4-Tiny algorithm to detect the Region of Interest (ROI) and the EfficientNetV2-S architecture freshness classification on Gurami fish images. The training process using both methods can produce an ROI detection model with a mean average precision of 93,58% and a classification model with an accuracy rate of 93%

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