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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Transformatika INTECOMS: Journal of Information Technology and Computer Science Journal of Computing Theories and Applications
Jutono Gondohanindijo, Jutono
Fakultas Ilmu Komputer,UNAKI, Semarang
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine Gomiasti, Fita Sheila; Warto, Warto; Kartikadarma, Etika; Gondohanindijo, Jutono; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10106

Abstract

This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal with non-linear problems. At the same time, hyperparameter tuning is done through Random Grid Search to find the best combination of parameters. Where the best parameter settings are C = 10, Gamma = 10, Probability = True. Test results show that the tuned SVM improves accuracy, precision, specificity, and F1 score significantly. However, there was a slight decrease in recall, namely 0.02. Even though recall is one of the most important measuring tools in disease classification, especially in imbalanced datasets, specificity also plays a vital role in avoiding misidentifying negative cases. Without hyperparameter tuning, the specificity results are so poor that considering both becomes very important. Overall, the best performance obtained by the proposed method is 0.99 for accuracy, 1.00 for precision, 0.98 for recall, 0.99 for f1-score, and 1.00 for specificity. This research confirms the potential of tuned SVMs in addressing complex data classification challenges and offers important insights for medical diagnostic applications.
Decision Tree Implementation in IT Job Recommendation System Widayati, Yohana Tri; Widjaja, Stephanus; Wicaksono, Adityo Putro; Gondohanindijo, Jutono; Putri, Christine Cecillia
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.8328

Abstract

Employment is the primary activity that humans engage in to generate income. With the advancement of technology and research, there are many new job opportunities leading to confusion in choosing a job path. This leads to individual confusion in making job choices. Ignorance of one's own talents and personality, as well as ignorance of the various options available, can be the source of this ignorance. This research aims to develop a Decision Tree model to assist users in determining the appropriate IT field. The system uses AI Project Cycle and data processing tools such as Google Collaboratory, which is based on Python programming language. The results show that the Decision Tree algorithm can be applied to recommend jobs in the IT field to help users find suitable fields in the IT field.
Implementasi Sistem Informasi Kepegawaian Non-ASN Berbasis Website Menggunakan Codeigniter 3 Pada Diskominfo Jawa Tengah Insiyyah, Insiyyah; Dharmawan, Alexander; Gondohanindijo, Jutono
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 4 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i4.10677

Abstract

Dinas Komunikasi dan Informatika Provinsi Jawa Tengah merupakan badan pemerintah yang berperan penting dalam mengelola informasi dan komunikasi di wilayah Jawa Tengah, Selain Aparatur Sipil Negara (ASN), Diskominfo Jateng juga dibantu oleh banyak tenaga non-ASN yang berperan penting dalam menunjang kelancaran operasional dan pelayanan publik. Proses penginputan data pegawai non-ASN masih menggunakan cara manual dengan menggunakan microsoft excel, cara ini dinilai kurang optimal dan menghambat pengambilan keputusan terkait pelaporan karyawan. Oleh karena itu, dibutuhkan sistem pengelolaan SDM berbasis website yang lebih efektif dan efisien untuk mempermudah dalam pengambilan kepustusan.Perancangan website menggunakan diagram UML (Unified Modelling Language) pembuatan website menggunakan bahasa pemrograman PHP (Hypertext Prepocessor) dan framework CodeIgniter 3. menggnakan pengujian dengan black box diperoleh hasil sesuai dengan harapan. Dengan adanya sistem ini diharapkan dapat membantu pengelolaan sistem informasi kepegawaian non-ASN pada Diskominfo Jateng.
Perancangan Sistem Informasi Berita Otomotif Berbasis Website Dengan Php Dan Mysql Tiert, Baruch Daniel; Dharmawan, Alexander; Gondohanindijo, Jutono
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 4 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i4.10955

Abstract

In today's digital era, information about the automotive world is in great demand by the public. To meet these needs, an information system is needed that is able to present automotive news quickly, accurately, and easily accessible. This research aims to design and develop a website-based automotive news information system using PHP and MySQL. This system is designed to facilitate users in getting the latest information about the automotive world, including news about new vehicles, product reviews, maintenance tips, and the latest technology. The methods used in developing this system include needs analysis, system design, implementation, and testing. The results show that the information system developed is able to provide automotive news services with a user-friendly interface and features that suit user needs. With this system, it is expected to increase the accessibility and dissemination of automotive information among the public.
Sentimen Analisis Aplikasi Digitalent Mobile Menggunakan Naïve Bayes Dan SVM Dengan Ekstraksi Fitur TT-IDF Putra, Jeremi Azero; Dharmawan, Alexander; Gondohanindijo, Jutono
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 4 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i4.11110

Abstract

Penelitian ini membahas penerapan analisis sentimen pada ulasan aplikasi mobile menggunakan algoritma Naïve Bayes dan Support Vector Machine (SVM) dengan ekstraksi fitur TF-IDF (Term Frequency-Inverse Document Frequency). Analisis sentimen adalah proses penggalian informasi dari teks untuk menentukan opini yang terkandung di dalamnya, yang berguna bagi pengembang aplikasi untuk memahami umpan balik pengguna. Dataset ulasan aplikasi mobile berjumlah 378 ulasan yang dikumpulkan dan dibersihkan sebelum diekstraksi fiturnya menggunakan metode TF-IDF, yang mengukur pentingnya sebuah kata dalam dokumen relatif terhadap kumpulan dokumen. Selanjutnya, dua algoritma pembelajaran mesin, Naïve Bayes dan SVM, diterapkan untuk membangun model klasifikasi sentimen. Kinerja model dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score dari hasil pengujian confussion matrix. Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes mencapai akurasi 85.71%, sedangkan SVM mencapai akurasi 82.14%. Tujuan dari penelitian ini adalah menekankan pentingnya pemilihan algoritma dan teknik ekstraksi fitur dalam analisis sentimen aplikasi mobile, serta memberikan informasibagi pengembang dalam meningkatkan kualitas aplikasi berdasarkan umpan balik pengguna.
Dashboard Pemantauan Inventori Pada Mixue Erlangga Simpang Lima Berbasis Web Jaya, Theofilus Palaun; Prihati, Yani; Gondohanindijo, Jutono
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 5 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i5.11407

Abstract

This research aims to develop a web-based inventory monitoring dashboard for Mixue Erlangga Simpang Lima. The dashboard is designed to provide ease in monitoring and managing stock in real-time, thereby enhancing operational efficiency and reducing the risk of stockouts or overstocking. The research methodology includes needs analysis, system design, development, and application testing. The result of this research is a dashboard that can display inventory data visually and interactively, and provide notifications when stock levels approach predefined minimum or maximum limits. The implementation of this dashboard is expected to assist management in making quicker and more accurate decisions regarding inventory management. User evaluations indicate that this dashboard successfully improves efficiency and effectiveness in monitoring and managing stock at Mixue Erlangga Simpang Lima.
Implementasi Metode Waterfall Pada Sistem Informasi Inventaris Barang Berbasis Web Di Hotel Grand Edge Semarang Widodo, Tulus Suryo; Prihati, Yani; Gondohanindijo, Jutono
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 5 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i5.11842

Abstract

A company asset, inventory management must always be monitored for its existence and condition. Inventory management of a company is very important. The stages of the waterfall software development method are used as the basis for the structure of requirements analysis, design, implementation, and system testing. Employee input data, item input data, and purchase input data are some parts of the designed information system. By choosing an efficient Rapid Application Development (RAD) approach, a Web-based Goods Inventory Information System was developed to solve the problem of errors and data duplication. The research methodology consists of observation, interviews, literature research. The purpose of this research is to design a web-based inventory information system to help manage inventory data at Grand Edge Hotel Semarang.
Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Warto, Warto; Gondohanindijo, Jutono; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11638

Abstract

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.
Analisis Akurasi dan Waktu Proses Deteksi Sentimen Menggunakan Image Mel-Spectrogram Gondohanindijo, Jutono
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13906

Abstract

Dalam upaya meningkatkan interaksi manusia-mesin, penelitian deteksi sentimen sudah banyak dilakukan peneliti untuk tujuan tersebut. Seiring dengan berkembangnya Mesin Pembelajaran, penelitian ini akan membandingkan kemampuan empat model klasifikasi : CNN, CRNN, SVM, dan MLP—dalam mengidentifikasi sentimen berbasis gambar Mel-spectrogram. Penelitian ini memanfaatkan representasi Mel-Spectrogram dari 640 sampel image ( gambar ) spektrogram yang mencakup delapan kelompok kelas sentimen berbeda. Setelah melalui tahap praproses data gambar dan ekstraksi fitur, kinerja model dievaluasi menggunakan validasi silang 10-fold serta metrik akurasi, presisi, recall, dan F1-score. CNN dan CRNN mencapai akurasi tertinggi (100%), sedangkan SVM dan MLP mencapai 99,22%. Dari sisi waktu pelatihan, SVM membutuhkan waktu paking sedikit, yaitu sebesar 0,45 detik. Penelitian ini bertujuan untuk mengetahui efektivitas pendekatan image (gambar) Mel-Spectrogram dan menegaskan perlunya pertimbangan trade-off antara akurasi tinggi dan efisiensi komputasi dalam pemilihan model. Kata Kunci – Analisis, Mel-Spectogram, Sentimen, Waktu Proses
Analysis Kernel and Feature: Impact on Classification Performance on Speech Emotion Using Machine Learning Gondohanindijo, Jutono; Noersasongko, Edi; Pujiono, Pujiono; Muljono, Muljono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29022

Abstract

The main objective of this study is to test the machine learning kernel's selection against the characteristics of the data set used, resulting in good classification performance. The goal of speech emotion recognition is to improve computers' ability to detect and process human emotions in order to improve their ability to respond to interactions between people and computers. It can be applied to feedback on talks, including sentimental or emotional content, as well as the detection of human mental health. One field of data mining work is Speech Emotion Recognition. One of the important things in data mining research is to determine the selection of the kernel Classifier, know the characteristics of datasets, perform Engineering Features and combine features and Corpus Datasets to obtain high accuracy. The research uses analysis and comparison methods using private and public datasets to detect speech emotions. Experimental analysis was done on the characteristics of datasets, selection of kernel classifiers, pre-processing, feature and corpus datasets fusion. Understanding the selection of a classifier kernel that matches the characteristics of the dataset, engineering features and the merger of features and datasets are the contributions of this investigation to improving the accuracy of the classification of speech emotion data. For models with the selection of kernels that match the characteristics of their datasets, this study gave an increase in accuracy of 12.30% for the private dataset and 14.80% for the public dataset, with accuracies of 100.00% and 74.80% respectively. Combining features and public datasets provides an increase in accuracy of 33.62% with an accuracy of 73.95%.
Co-Authors De Rosal Ignatius Moses Setiadi Dharmawan, Alexander Edi Noersasongko Etika Kartikadarma Gomiasti, Fita Sheila Insiyyah, Insiyyah Jaya, Theofilus Palaun Muljono Muljono Muslikh, Ahmad Rofiqul Ojugo, Arnold Adimabua Prihati, Yani Pujiono Pujiono Putra, Jeremi Azero Putri, Christine Cecillia Syahroni Wahyu Iriananda, Syahroni Wahyu Tiert, Baruch Daniel Warto - Wicaksono, Adityo Putro Widayati, Yohana Tri Widjaja, Stephanus Widodo, Tulus Suryo