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All Journal International Journal of Electrical and Computer Engineering Jurnal Manajemen dan Organisasi TEKNIK INFORMATIKA Jurnal Ilmiah Teknologi dan Rekayasa TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology) Syntax Literate: Jurnal Ilmiah Indonesia InComTech: Jurnal Telekomunikasi dan Komputer ComTech: Computer, Mathematics and Engineering Applications Jurnal Optimalisasi Indonesian Journal of Electrical Engineering and Computer Science TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Ilmu Komputer dan Informatika Eduvest - Journal of Universal Studies Innovative: Journal Of Social Science Research
Suharjito Suharjito, Suharjito
Bina Nusantara University
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Journal : Jurnal Ilmiah Teknologi dan Rekayasa

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SENTIMENT ANALYSIS OF INDONESIA’S DIGITAL WALLET USING COMBINATION MACHINE LEARNING AND EMOTICON WEIGHT Tinambunan, Gusmariani; Suharjito, Suharjito
Jurnal Ilmiah Teknologi dan Rekayasa Vol 26, No 3 (2021)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2021.v26i3.5266

Abstract

Opinions on social media can be used to determine user sentiment by using sentiment analysis concept. Sentiment analysis requires several important stages, namely, preprocessing, feature extraction and classification method stages. The preprocessing stage was carried out to eliminate inconsistent data. In previous research, punctuation marks removal was applied at the preprocessing stage which can eliminate the emoticon position. Emoticons are a combination of punctuation marks. According to previous research, the emoticon feature has no contribution in sentiment analysis. There is another suggestion to maintain an emoticon position like converting an emoticon into a more relevant word such as :) into a “smile”. However, the feature of emoticon weights has not been considered in the sentiment analysis process. In order to consider the role of emoticons and to improve sentiment analysis performance, we propose using a combination of machine learning and emoticon weights. We perform emoticon weight based on probability and sentiment score. Each probability value and sentiment score of the emoticon will be normalized using the z-score method. There are several machine learning methods that have the best classification success rates, namely, Naïve Bayes and SVM. Based on the evaluation results of the proposed model, the best accuracy is 87% - 89% when using the combination of machine learning and emoticon sentiment score. Based on the results also show that the emoticon sentiment score has a significant effect on the accuracy of sentiment analysis.
PERBAIKAN MODEL ALEXNET UNTUK MENDETEKSI KEMATANGAN TBS KELAPA SAWIT DENGAN MENGGUNAKAN IMAGE ENHANCEMENT DAN HYPERPARAMETER TUNING Indra Alfredo; Suharjito Suharjito
Jurnal Ilmiah Teknologi dan Rekayasa Vol 27, No 1 (2022)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2022.v27i1.5973

Abstract

Kualitas CPO yang baik adalah dihasilkan dari buah sawit yang mempunyai tingkat kematangan yang baik. Pada umumnya penentuan kematangan TBS kelapa sawit dilakukan melalui penilaian warna buah secara visual dan subjektif, sehingga perlu dikembangkan suatu model untuk mengidentifikasi tingkat kematangan berdasarkan karakteristik warna. Adapun tujuan dari penelitian ini adalah untuk mengembangkan sebuah model deep learning agar mendapatkan hyperparameter terbaik dari model yang diteliti yaitu AlexNet untuk mengklasifikasi tingkat kematangan. Jumlah dataset yang digunakan terdiri dari 6.000 buah gambar kelapa sawit dengan enam tingkat kematangan. Teknik augmentation akan digunakan untuk membantu memperbanyak jumlah dataset, selain itu menambahkan parameter image enhancement untuk mencerahkan gambar agar lebih nyata. Parameter lainnya menggunakan binary crossentropy untuk mengurangi loss dan optimizer menggunakan Stochastic Gradient Descent (SGD) untuk menemukan nilai optimal. Kemudian dari hasil evaluasi initial model dilakukan hyperparameter tuning untuk mendapatkan optimal parameter dari model AlexNet yang dibangun. Dari hasil penelitian ini menunjukkan bahwa model yang diajukan menggunakan metode Convolutional Neural Network (CNN) dengan menggunakan model AlexNet akurasi meningkat setelah menggunakan hyperparameter tuning dan image enhancement berhasil mencapai 0.9530.
Co-Authors Abba Suganda Girsang, Abba Suganda Abdul Latief Qohar Abimanyu Dharma Poernomo Andika, Rizky Andriyansyah, Andriyansyah Arta, Mikhael Chandra Asroll, Muhammad Astrid Lestari Tungadi Aulia, Yunita Bambang Haryanto Christianto, Yohanes Demi Adidrana Dian Puspita Tedjosurya, Dian Puspita Dino Rimantho Halim, Marwa Harisapto, Kukuh Ida Ratna, Ida Imran, Bahtiar Indra Alfredo Ishartati Jimmy Jimmy Josef Bernadi, Josef Kurnianto, Benedict Ariel Leonardus Hardjo, Leonardus Lumbantobing, Hariman Machfud Machfud Marimin Marimin Maulivia, Vivia Surya Muhammad Rifqi Nababan, Nidia Pialina Nensi, Sri Wahyu Nico Surantha, Nico Nur Anwar, Nur Perdana, Panji Arief Prayogo, Rudy Hartono Putri, Yulia Aneke Ratna Avianti, Irma Raymond Raymond Rian Rassetiadi S, Katherina Meylda Loy Sibarani, Imelda Juliana Br. Stefani Agusta Sukardi Sukardi Tinambunan, Gusmariani Widodo, Bambang Yundari, Yundari Yuwono Abdillah