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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Khatulistiwa Informatika SMARTICS Journal CYBERNETICS BULETIN AL-RIBAATH PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Teknika Teknosains : Jurnal Sains,Teknologi dan Informatika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) INSERT: Information System and Emerging Technology Journal JUTECH : Journal Education and Technology JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) SmartComp
Rachmat Wahid Saleh Insani
Universitas Muhammadiyah Pontianak
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

 1 
Pemodelan dan Verifikasi Formal Protokol EE-OLSR dengan UPPAAL CORA Rachmat Wahid Saleh Insani; Reza Pulungan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 10, No 1 (2016): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.11192

Abstract

Information and Communication Technology systems is a most important part of society.  These systems are becoming more and more complex and are massively encroaching on daily life via the Internet and all kinds of embedded systems. Communication protocols are one of the ICT systems used by Internet users. OLSR protocol is a wireless network communication protocol with proactive, and based on link-state algorithm. EE-OLSR protocol is a variant of OLSR that is able to prolong the network lifetime without losses of performance.Protocol verification process generally be done by simulation and testing. However, these processes unable to verify there are no subtle error or design flaw in protocol. Model Checking is an algorithmic method runs in fully automatic to verify a system. UPPAAL is a model checker tool to model, verify, and simulate a system in Timed Automata.UPPAAL CORA is model checker tool to verify EE-OLSR protocol modelled in Linearly Priced Timed Automata, if the protocol satisfy the energy efficient property formulated by formal specification language in Weighted Computation Tree Logic syntax. Model Checking Technique to verify the protocols results in the protocol is satisfy the energy efficient property only when the packet transmission traffic happens.
COMPARISON OF CNN MODELS WITH TRANSFER LEARNING IN THE CLASSIFICATION OF INSECT PESTS Angga Prima Syahputra; Alda Cendekia Siregar; Rachmat Wahid Saleh Insani
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 1 (2023): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.80956

Abstract

Insect pests are an important problem to overcome in agriculture. The purpose of this research is to classify insect pests with the IP-102 dataset using several CNN pre-trained models and choose which model is best for classifying insect pest data. The method used is the transfer learning method with a fine-tuning approach. Transfer learning was chosen because this technique can use the features and weights that have been obtained during the previous training process. Thus, computation time can be reduced and accuracy can be increased. The models used include Xception, MobileNetV3L, MobileNetV2, DenseNet-201, and InceptionV3. Fine-tuning and freeze layer techniques are also used to improve the quality of the resulting model, making it more accurate and better suited to the problem at hand. This study uses 75,222 image data with 102 classes. The results of this study are the DenseNet-201 model with fine-tuning produces an accuracy value of 70%, MobileNetV2 66%, MobileNetV3L 68%, InceptionV3 67%, Xception 69%. The conclusion of this study is that the transfer learning method with the fine-tuning approach produces the highest accuracy value of 70% in the DenseNet-201 model.
Co-Authors Alamsyah, Dedi Alda Cendekia Siregar Alkadri, Syarifah Putri Agustini Angga Prima Syahputra Anggreini, Sella Arfiyan, Fandika Roinaldi Barry Ceasar Octariadi Barry Ceasar Octariadi Dedy Trianto Eko Julianto Elsa Damayanti Gusti, Gusti Muhamad Adzaky I Dewa Ketut Rai Rustina Masroni MHD. Reza M.I. Pulungan Muhammad Agus Muljanto Muhammad Hendri Nasim, Muhammad PRAYOGI, ADITYA PUTU SUGIARTAWAN Reyhan Siregar, Alda Cendekia Siregar, Alda Cendekia Sucipto Sucipto Sucipto Sucipto Sucipto, Sucipto Sumirah Sumirah Sumirah Sumirah Syarifah Putri Agustini, Syarifah Putri Vika Ummu Hani Wahyu Fajar Wicaksono Wahyu Nugroho Ya' Ilham Eka Saputra