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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Jurnal CoreIT Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining JMM (Jurnal Masyarakat Mandiri) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Abdimasku : Jurnal Pengabdian Masyarakat Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Innovation in Research of Informatics (INNOVATICS) Journal of Soft Computing Exploration Journal of Information System Exploration and Research Scientific Journal of Informatics
Jumanto Unjung
Department Of Computer Science, Universitas Negeri Semarang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Halal Food Restaurant Classification Based on Restaurant Review in Indonesian Language Using Machine Learning Hidayat, Nurul; Hakim, M. Faris Al; Jumanto, Jumanto
Scientific Journal of Informatics Vol 8, No 2 (2021): November 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i2.33395

Abstract

Purpose: Halal tourism or muslim friendly tourism has big potential for the tourism industry in Indonesia. According to Cresent Rating, the world’s leading authority on halal-friendly travel, one of the indicators for halal tourism is the availability of choices for halal foods. To support halal tourism, unfortunately, not all restaurants around the tourism object or in the city where the tourism object is located have labels or information that makes people know about halal food in the restaurant easily.Methods/Study design/approach: The data in this research was obtained from online media such as Google Maps, TripAdvisor, and Zoomato. The data consists of 870 data with the classification of halal food restaurants and 590 data with the reverse classification. Machine learning methods were chosen as classifiers. Some of them were Naive Bayes, Support Vector Machine, and K-Nearest Neighbor. Result/Findings: The result from this research shows that the proposed method achieved an accuracy of 95,9% for Support Vector Machine, 93,8% for Multinomial Naive Bayes, and 91% for K-Nearest Neighbor. In the future, our result will be to support the halal tourism environment in terms of technology. Novelty/Originality/Value: In this study, we utilize restaurant reviews done by visitors to get information about the classification of halal food restaurants.
Pembelajaran STEM Berbasis Robotika Sederhana Bagi Guru Sekolah Dasar di Karimunjawa Riza Arifudin; Abas Setiawan; Zaenal Abidin; Devi Ajeng Efrilianda; Jumanto Jumanto
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 5, No 3 (2022): September 2022
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/ja.v5i3.825

Abstract

STEM adalah inovasi pembelajaran yang dirancang untuk memberikan siswa pengalaman belajar yang bermakna. STEM merupakan singkatan dari science, technology, engineering dan Mathematic. Istilah tersebut mengacu pada pendekatan pembelajaran yang mengintegrasikan empat disiplin ilmu ke dalam satu proses pembelajaran. Hal ini dimaksudkan untuk mendorong siswa untuk berpikir kritis, menyeluruh dan inovatif ketika menemukan solusi untuk masalah mereka. Robot Edukasi adalah alat pembelajaran yang efektif untuk pembelajaran berbasis proyek yang mengintegrasikan STEM, pengkodean, pemikiran komputasi, dan keterampilan teknik ke dalam satu proyek. Robotika memberi siswa kesempatan untuk mengeksplorasi bagaimana teknologi bekerja dalam kehidupan nyata. Jenis robot yang digunakan adalah robot dengan panel surya dengan enam variasi bentuk. Peserta pada pengabdian ini adalah guru-guru kelas di SD Negeri 1 Karimunjawa. Peserta sangat antusias mengikuti rangkaian kegiatan ini. Peserta dapat merakit robot sesuai dengan bentuk yang diingatkan dalam kelompoknya. Salah satu yang terpenting pada pengabdian ini, dengan menggunakan robot dapat membuat lingkungan belajar yang menyenangkan dan mengasyikkan melalui integrasi sifat praktis dan teknologinya. Lingkungan belajar yang menarik memungkinkan peserta didik untuk mempelajari semua keterampilan dan pengetahuan yang dibutuhkan untuk mencapai tujuan mereka guna menyelesaikan proyek yang diminati. Para guru sangat menyambut positif dan antusias sekali dalam melakukan kegiatan ini. Kata kunci: Pembelajaran STEM, robot edukasi, guru
KLASIFIKASI PENYAKIT DIABETES MENGGUNAKAN ALGORITMA NAIVE BAYES Anisa, Devi Nurul; Jumanto, Jumanto
Dinamika Informatika : Jurnal Ilmiah Teknologi Informasi Vol 14 No 1 (2022)
Publisher : Fakultas Teknologi Informasi Universitas Stikubank (Unisbank) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (678.905 KB) | DOI: 10.35315/informatika.v14i1.9135

Abstract

Penyakit diabetes adalah suatu penyakit gangguan metabolik yang di tandai oleh tingginya gula darah yang melebihi nilai normal. Terdapat banyak faktor yang menjadi penyebab penyakit diabetes, faktor-faktor tersebut diantaranya seperti faktor keturunan, berat badan, usia, dan faktor lainnya. Banyak yang tidak menyadari bahwa dirinya terkena penyakit diabetes, sehingga angka kematian yang disebabkan oleh penyakit diabetes ini semakin banyak dan setiap tahunnya diperkirakan akan terus meningkat angka kasus kematiannya. Maka dari itu penelitian ini mencoba menerapkan suatu metode klasifikasi untuk memprediksi apakah seseorang terkena diabetes atau tidak. Dataset yang digunakan pada penelitian ini merupakan data yang di dapatkan dari data Kaggle, yaitu Predict diabetes based on diagnostic measure. Metode klasifikasi yang digunakan yaitu dengan menerapkan algoritma Naive Bayes yang mampu menghasilkan akurasi yang baik. Hasil dari penelitian ini di dapati nilai akurasi 92%. Hasil ini lebih baik dibanding dengan penelitian sebelumnya yang menggunakan k-nearest neighbor (KNN) dengan tingkat akurasi sebesar 91%.
Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction Tiara Lailatul Nikmah; Jumanto Jumanto; Budi Prasetiyo; Nina Fitriani; Much Aziz Muslim
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26366

Abstract

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P  lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default.  Defaults on  P2P  lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms.
Optimized Handwriting-based Parkinson's Disease Classification Using Ensemble Modeling and VGG19 Feature Extraction Jumanto Unjung; Maylinna Rahayu Ningsih
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.47108

Abstract

Purpose: Parkinson's is a neurological disorder that causes muscles to weaken and arms and legs to tremor over time. The discovery and identification of the stages of Parkinson's disease can substantially benefit the treatment of its symptoms. Many studies have been conducted in classifying hand-drawn -based Parkinson's disease but the resulting performance is still not optimal. The purpose of this research is to improve the performance of handwritten Parkinson's disease classification accuracy using an ensemble soft voting model.Methods: The model adopted the Parkinson's Disease Augmented Data of Handwritten dataset taken from the Kaggle Repository, where the dataset contains Healthy and Parkinson's classes with a total of 3264 images. Then, the dataset was augmented again, resulting in 2612 images in the training data and 652 images in the validation data. After that, the dataset was optimized with VGG19 feature extraction and fine-tuning and then modeled with the Ensemble Learning model. The model was evaluated with a confusion matrix, and the classification report was viewed.Result: The results of the proposed evaluation test discovered the effectiveness of the Ensemble Learning model with Fine Tuning VGG19 feature extraction by producing an accuracy of 98.9%.Novelty: This research has successfully contributed to the proposed ensemble model for Parkinson's handwriting and improved the accuracy performance of the model.
Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings Rofik Rofik; Roshan Aland Hakim; Jumanto Unjung; Budi Prasetiyo; Much Aziz Muslim
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3566

Abstract

Online job searching is one of the most efficient ways to do this, and it is widely used by people worldwide because of the automated process of transferring job recruitment information. The easy and fast process of transferring information in job recruitment has led to the rise of fake job vacancy fraud. Several studies have been conducted to predict fake job vacancies, focusing on improving accuracy. However, the main problem in prediction is choosing the wrong parameters so that the classification algorithm does not work optimally. This research aimed to increase the accuracy of fake job vacancy predictions by tuning parameters using GridSearchCV. The research method used was SVM and Gradient Boosting with parameter adjustments to improve the parameter combination and align it with the predicted model characteristics. The research process was divided into preprocessing, feature extraction, data separation, and modeling stages. The model was tested using the EMSCAD dataset. This research showed that the SVM algorithm can achieve the highest accuracy of 98.88%, while gradient enhancement produces an accuracy of 98.08%. This research showed that optimizing the SVM model with GridSearchCV can increase accuracy in predicting fake job recruitment.
Classification Email Spam using Naive Bayes Algorithm and Chi-Squared Feature Selection Ningsih, Maylinna Rahayu; Unjung, Jumanto; Farih, Habib al; Muslim, Much Aziz
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.9695

Abstract

Spam email is a problem that disturbs and harms the recipient. Machine learning is widely used in overcoming email spam because of its ability to classify emails into spam or non-spam. In this research, the Naïve Bayes algorithm is initiated with the Chi-Squared selection feature to classify spam emails. So that the implementation is able to increase accuracy for better performance in classification. The feature selection method is used to direct the model's attention to features that are related to the target variable. In this study, the chi squared feature uses a value of K = 2500, with an accuracy of 98.83% which shows an increase in model performance compared to previous research. So that the Naïve Bayes model with the Chi-Squared selection feature is proven to provide better performance. 
Sentiment based-emotion classification using bidirectional long short term-memory (Bi-LSTM) Utami, Putri; Ningsih, Maylinna Rahayu; Pertiwi, Dwika Ananda Agustina; Unjung, Jumanto
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.461

Abstract

Social media is now an important platform for sharing information, expressing opinions, and daily feelings or emotions. The expression of emotions such as anger, sadness, fear, happiness, disappointment, and so on social networks can be further analyzed either for business purposes or just analyzing the habits of a community or someone's posts.  However, analyzing manually will be a time-consuming process, and the use of conventional methods can affect the results of less accurate accuracy. This research aims to improve the accuracy of recognizing emotions in text by using the Bidirectional Long Short Term Memory (Bi-LSTM) method, which is a subset of RNNs that tend to be more stable during training and show better performance on various NLP and other processing tasks. The method used includes several stages, namely preprocessing, tokenization, sequence padding, and modeling. The results of this study show that the Bi-LSTM model is capable of predicting emotions in text with an accuracy of 94.45% because it excels in handling the temporal context and can avoid vanishing gradients.
STRATEGI BRANDING DIGITAL MARKETING GUNA PENINGKATAN PENERIMAAN SISWA BARU BAGI SEKOLAH BARU JENJANG SMP M. Faris Al Hakim; Prabowo Yudho Jayanto; Jumanto Jumanto; Heri Ardiyanto
JMM (Jurnal Masyarakat Mandiri) Vol 8, No 1 (2024): Februari
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v8i1.20499

Abstract

Abstrak: Sekolah Menengah Pertama Islam Terpadu (SMP IT) Mutiara Hati merupakan sekolah tingkat menengah baru yang mengusung program unggulan khusus Tahfizul Qur’an (Hafalan Qur’an) di Kecamatan Gunungpati Kota Semarang. Salah satu tantangan besar yang dihadapi oleh mitra adalah upaya untuk mendapatkan siswa baru. SMP IT Mutiara Hati Semarang sebagai mitra telah melakukan publikasi melalui media sosial meskipun belum optimal. Selain itu, teknologi informasi yang mendukung program penerimaan siswa baru juga masih terbatas. Hal tersebut menjadi dasar perlunya peningkatan pemahaman terhadap digital marketing dan penerapan suatu sistem informasi. Metode yang digunakan dalam pelaksanaan program terdiri dari identifikasi permasalahan, pelaksanaan, dan evaluasi. Pada tahap pelaksanaan, kegiatan pelatihan digital marketing dan pengembangan sistem penerimaan siswa baru dilaksanakan secara bersamaan. Umpan balik mitra melalui kuisioner menjadi instrumen dalam mengevaluasi program. Hasil yang diperoleh dari pelaksanaan program yaitu digital marketing dapat menjadi strategi bagi mitra dalam memperkuat upaya pengenalan instansi kepada masyarakat luas. Seluruh peserta pelatihan telah memahami dengan baik pemanfaatan digital marketing. Kegiatan pengabdian ini juga menghasilkan produk berupa sistem informasi berbasis web guna mendukung program penerimaan siswa baru pada instansi mitra. Hasil pengabdian ini juga menunjukkan bahwa mitra telah mampu menentukan prioritas sistem informasi yang tepat bagi sekolah.Abstract: Sekolah Menengah Pertama Islam Terpadu (SMP IT) Mutiara Hati is a new secondary school that carries a special flagship program Tahfizul Qur'an (Qur'an memorization) in Gunungpati District, Semarang City. One of the big challenges faced by partners was trying to get new students. SMP IT Mutiara Hati Semarang as the partner had published via social media, although it was not yet optimal. Apart from that, information technology that supported new student admission programs was also still limited. This was what underlied the need to increase understanding of digital marketing and the application of information systems. The method used in implementing the program consisted of problem identification, implementation and evaluation. At the implementation stage, digital marketing training activities and development of a new student admission system were carried out simultaneously. Partner feedback through questionnaires became an instrument in evaluating the program. The results obtained from implementing the program was that digital marketing can be a strategy for partners in strengthening efforts to introduce the institution to the wider community. All training participants have gained a good understanding of the use of digital marketing. This service community activity also produced a product in the form of a web-based information system to support new student admission programs at partner institution. The results of this service also showed that partners had been able to determine the right information system priorities for schools. 
Optimized Support Vector Machine with Particle Swarm Optimization to Improve the Accuracy Amazon Sentiment Analysis Classification Ningsih, Maylinna Rahayu; Unjung, Jumanto; Pertiwi, Dwika Ananda Agustina; Prasetiyo, Budi; Muslim, Much Aziz
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 1, February 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i1.1888

Abstract

Text mining is a valuable technique that empowers users to gain a deeper understanding of existing textual data, ultimately allowing them to make more informed decisions. One important application of text mining is in the field of sentiment analysis, which has gained significant traction among companies aiming to understand how customers perceive their products and services. In response to this growing need, various research efforts have been made to improve the accuracy of sentiment analysis classification models. The purpose of this article is to discuss a specific approach using the Support Vector Machine (SVM) algorithm, which is often used in machine learning for text classification tasks and then combined with the application of Particle Swarm Optimization (PSO), which optimizes the SVM model parameters to achieve the best classification results. This dynamic combination not only improves accuracy but also enhances the model's ability to efficiently handle large amounts of text data to achieve better results. The research findings highlight the effectiveness of this approach. The application of the SVM algorithm with PSO resulted in an outstanding accuracy performance of 94.92%. The substantial increase in accuracy compared to previous studies shows the promising potential of this methodology. This proves that the SVM algorithm model approach with Particle Swarm Optimization provides good performance.
Co-Authors Abas Setiawan Ahmad, Kamilah Al Qohar, Bagus Alamsyah - Anisa, Devi Nurul Budi Prasetiyo Budi Prasetiyo, Budi Darmawan, Aditya Yoga Devi Ajeng Efrilianda Dimas Fanny Hebrasianto Permadi Dullah, Ahmad Ubai Endang Sugiharti, Endang Farih, Habib al Hakim, M. Faris Al Hakim, Roshan Aland Heri Ardiyanto Khalidah, Saidatul M. Faris Al Hakim M. Faris Al Hakim Maylinna Rahayu Ningsih Much Aziz Muslim Much Aziz Muslim Much Aziz Muslim Nina Fitriani Ningsih, Maylinna Rahayu Nurriski, Yopi Julia Nurul Hidayat Pertiwi, Dwika Ananda Agustina Prabowo Yudho Jayanto Purwoko, Agus Putri Utami, Putri Qohar, Bagus Al Rianto, Nur Azis Kurnia Riza Arifudin Riza Arifudin Rofik Rofik Rofik Rofik, Rofik Roshan Aland Hakim Sanjani, Fathimah Az Zahra Syamsudin, Hilmi Tanga, Yulizchia Malica Pinkan Tiara Lailatul Nikmah Utami, Annisaa Wibowo, Kevyn Alifian Hernanda Zaenal Abidin