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All Journal Techno.Com: Jurnal Teknologi Informasi CALYPTRA : Jurnal Ilmiah Mahasiswa Universitas Surabaya Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Information Systems Engineering and Business Intelligence Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika dan Multimedia Indonesian Journal of Information System Keluwih: Jurnal Sains dan Teknologi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Teknika J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika dan Multimedia Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi)
Mohammad Farid Naufal, Mohammad Farid
Jurusan Teknik Informatika, Universitas Surabaya, Surabaya, Jawa Timur
Aerospace Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Transportation

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Rekomendasi Peralatan Camping Menggunakan Metode Complex Proportional Assesment Naufal, Mohammad Farid; Prasetyo, Daniel Hary; Ramadhan, Firman Herda
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.503

Abstract

Boss camping is a company engaged in services, namely the rental of camping equipment. Because it is engaged in goods rental services, the company uses a social media operating system to carry out its operational activities. The system is expected to make it easier to rent and provide real-time equipment information so that customers do not need to go to the rental place to check the stock of equipment to be borrowed, so as to make it easier for customers who will rent climbing equipment. In addition, this system is able to assist admins in managing data, so as to provide valid and accurate information. The decision support system was obtained using the Copras (Complex Proportional Assessment) method. The Copras method is applied in a decision support system in order to solve the problem because it is used to calculate the utility of a predetermined alternative for comparison and calculate the evaluation of the maximum and minimum criteria separately
BloodCell-YOLO: Efficient Detection of Blood Cell Types Using Modified YOLOv8 with GhostBottleneck and C3Ghost Modules Naufal, Mohammad Farid; Ferdiana Kusuma, Selvia
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 1 (2025): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.1.41-52

Abstract

Background: Diagnosing many medical ailments, including infections, immunological problems, and hematological diseases, is a process that depends on precise as well as quick identification of blood cell. Conventional methods for blood cell identification may include skilled pathologists visually inspecting the cell under a microscope, which is a time-consuming choreography. This method is not appropriate for processing vast amounts of data, because the process is time-consuming and is prone to human mistakes. Objective: This study aimed to improve YOLOv8 architecture, offering a more efficient and simplified model for blood cell identification. In addition, the main objective of the analysis was to reduce computational load as well as amount of parameters and still maintaining or improving detection performance. Methods: GhostBottleneck and C3Ghost modules used in the study were included in the head and backbone of YOLOv8 architecture for improvement. All versions of YOLOv8 was subjected to the changes including n, s, m, l, and x. During the analysis, the efficacy of the recommended method was evaluated using a dataset of seven kinds of blood, namely basophil, eosinophil, lymphocyte, monocyte, neutrophil, platelets, and red blood cells (RBCs). The analysis also tested the proposed method on the well-known Blood Cell Count and Detection (BCCD) dataset, which was a common benchmark in this field, for comparing the performance. Performance of the model relating to past studies was assessed through this process. Results: The investigation used GhostBottleneck and C3Ghost modules to reduce GFLOPS by 45.56% and the number of parameters by 76.55%. Mean average precision (mAP50) of 0.984 was achieved using recommended method. Additionally, on BCCD, the method scored 0.94 on New Cell Dataset. Conclusion: Modifications performed to YOLOv8 design significantly increased its blood cell detection efficiency and effectiveness. The improvements showed that the changed model was suitable for real-time use in settings with constrained resources. Keywords: Blood Cell Detection, C3Ghost, Ghostbottleneck, YOLOv8
Sentiment Analysis of ChatGPT on Indonesian Text using Hybrid CNN and Bi-LSTM Prasetyo, Vincentius Riandaru; Naufal, Mohammad Farid; Wijaya, Kevin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6334

Abstract

This study explores sentiment analysis on Indonesian text using a hybrid deep learning approach that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). Due to the complex linguistic structure of the Indonesian language, sentiment classification remains challenging, necessitating advanced methods to capture both local patterns and sequential dependencies. The primary objective of this research is to improve sentiment classification accuracy by leveraging a hybrid model that integrates CNN for feature extraction and Bi-LSTM for contextual understanding. The dataset consists of 800 manually labeled samples collected from social media platforms, preprocessed using case folding, stop word removal, and lemmatization. Word embeddings are generated using the Word2Vec CBOW model, and the classification model is trained using a hybrid architecture. The best performance was achieved with 32 Bi-LSTM units, a dropout rate 0.5, and L2 regularization, which was evaluated using Stratified K-Fold cross-validation. Experimental results demonstrate that the hybrid model outperforms conventional deep learning approaches, achieving 95.24% accuracy, 95.09% precision, 95.15% recall, and 95.99% F1 score. These findings highlight the effectiveness of hybrid architectures in sentiment analysis for low-resource languages. Future work may explore larger datasets or transfer learning to enhance generalizability.
Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca Naufal, Mohammad Farid
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 2: April 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021824553

Abstract

Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. Computer vision adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan self autonomous machine agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara real time. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari Machine Learning sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942. AbstractWeather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.
Pendeteksi Citra Masker Wajah Menggunakan CNN dan Transfer Learning Naufal, Mohammad Farid; Kusuma, Selvia Ferdiana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 6: Desember 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021865201

Abstract

Pada tahun 2021 pandemi Covid-19 masih menjadi masalah di dunia. Protokol kesehatan diperlukan untuk mencegah penyebaran Covid-19. Penggunaan masker wajah adalah salah satu protokol kesehatan yang umum digunakan. Pengecekan secara manual untuk mendeteksi wajah yang tidak menggunakan masker adalah pekerjaan yang lama dan melelahkan. Computer vision merupakan salah satu cabang ilmu komputer yang dapat digunakan untuk klasifikasi citra. Convolutional Neural Network (CNN) merupakan algoritma deep learning yang memiliki performa bagus dalam klasifikasi citra. Transfer learning merupakan metode terkini untuk mempercepat waktu training pada CNN dan untuk mendapatkan performa klasifikasi yang lebih baik. Penelitian ini melakukan klasifikasi citra wajah untuk membedakan orang menggunakan masker atau tidak dengan menggunakan CNN dan Transfer Learning. Arsitektur CNN yang digunakan dalam penelitian ini adalah MobileNetV2, VGG16, DenseNet201, dan Xception. Berdasarkan hasil uji coba menggunakan 5-cross validation, Xception memiliki akurasi terbaik yaitu 0.988 dengan waktu total komputasi training dan testing sebesar 18274 detik. MobileNetV2 memiliki waktu total komputasi tercepat yaitu 4081 detik dengan akurasi sebesar 0.981. AbstractIn 2021 the Covid-19 pandemic is still a problem in the world. Therefore, health protocols are needed to prevent the spread of Covid-19. The use of face masks is one of the commonly used health protocols. However, manually checking to detect faces that are not wearing masks is a long and tiring job. Computer vision is a branch of computer science that can be used for image classification. Convolutional Neural Network (CNN) is a deep learning algorithm that has good performance in image classification. Transfer learning is the latest method to speed up CNN training and get better classification performance. This study performs facial image classification to distinguish people using masks or not by using CNN and Transfer Learning. The CNN architecture used in this research is MobileNetV2, VGG16, DenseNet201, and Xception. Based on the results of trials using 5-cross validation, Xception has the best accuracy of 0.988 with a total computation time of training and testing of 18274 seconds. MobileNetV2 has the fastest total computing time of 4081 seconds with an accuracy of 0.981.
Natural Language Processing untuk Otomatisasi Pengenalan Pronomina dalam Kalimat Bahasa Indonesia Naufal, Mohammad Farid; Kusuma, Selvia Ferdiana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 5: Oktober 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022946394

Abstract

Pronomina (kata ganti) adalah jenis kata yang dapat dipakai untuk menggantikan posisi kata benda atau orang dalam suatu kalimat. Penggunaan pronomina akan mudah dipahami apabila serangkaian kalimat dibaca secara utuh. Namun jika rangkaian kalimat tersebut hanya dibaca pada kalimat-kalimat tertentu, maka akan sulit memahami kalimat yang memiliki pronomina. Pada pengolahan bahasa alamiah, diperlukan kejelasan konteks dari sebuah kalimat. Dalam konteks otomatisasi pengolahan bahasa alamiah, adanya pronomina dapat menyulitkan komputer untuk memahami kalimat tersebut. Oleh sebab itu, dalam pengolahan bahasa alamiah yang mengandung pronomina diperlukan pre proses berupa pengubahan pronomina ke dalam bentuk subjek atau objek asli yang dirujuk. Metode yang diusulkan untuk menyelesaikan permasalahan ini adalah pendekatan berbasis sintaktik. Pendekatan ini menitikberatkan pada struktur kata yang digunakan dan struktur komponen kata yang digunakan. Metode yang diusulkan memiliki 4 tahapan yakni pengumpulan data, pembangkitan aturan, otomatisasi pengenalan pronominal, dan terakhir adalah evaluasi.  Metode yang diusulkan telah diujicobakan untuk mengenali adanya pronomina dari kalimat-kalimat pada materi Ilmu Pengetahuan Alam dan Ilmu Pengetahuan Sosial di jenj­­ang sekolah dasar. Hasil evaluasi menunjukkan bahwa metode yang diusulkan dapat digunakan untuk mengubah subjek yang berbentuk pronomina menjadi subjek atau objek asli yang dirujuk. Rata-rata akurasi yang didapatkan sebesar 81%. Akurasi tersebut didapatkan dari perbandingan antara jumlah kata ganti yang berhasil diidentifikasi subjeknya dengan keseluruhan data uji. Hasil dari penelitian ini dapat digunakan peneliti di bidang Natural Language Processing untuk melakukan praproses terhadap teks yang akan diolah. AbstractA pronoun is a word that can be used to replace a noun or person in a sentence. The use of pronouns will be easy to understand if a series of sentences is read in its entirety. However, if the sentence series is only read in specific sentences, it will be difficult to understand sentences with pronouns. In natural language processing, it is necessary to clarify the context of a sentence. In the context of automation of natural language processing, the existence of pronouns can make it difficult for computers to understand the sentence. Therefore, in processing natural language containing pronouns, it is necessary to pre-process in the form of converting pronouns into the form of the original subject or object referred to. The method proposed to solve this problem is a syntactic-based approach. This approach focuses on the structure of the words used and the word components used. The proposed method has 4 stages, namely data collection, rule generation, automation of pronoun recognition and the last is evaluation. The proposed method has been evaluated to identify the existence of pronouns from sentences in the Natural Sciences and Social Sciences material at the elementary school level. The evaluation results show that the proposed method can be used to change the subject in the form of a pronoun into the original subject or object referred to. The average accuracy obtained is 81%. The accuracy is obtained from the comparison between the number of pronouns that have been identified with the overall test data. Researchers in natural language processing can use the results of this study to pre-process their text.  
Otomatisasi Pembangkitan Pertanyaan untuk Bahasa Indonesia (Systematic Literature Review) Naufal, Mohammad Farid; Kusuma, Selvia Ferdiana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 1: Februari 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106455

Abstract

Penelitian tentang otomatisasi pembangkitan pertanyaan terus berkembang. Berbagai metode telah coba diterapkan pada berbagai bahasa. Setiap bahasa memiliki karakteristik yang berbeda beda. Oleh karena itu, metode yang digunakan untuk membangkitkan pertanyaan juga harus disesuaikan dengan bahasa yang digunakan. Otomatisasi pembangkitan pertanyaan untuk bahasa Indonesia juga sudah mulai berkembang sejak 2015. Hasil penelitian-penelitian tersebut perlu dianalisis agar dapat mengetahui kelebihan maupun kekurangan dari setiap metode yang pernah digunakan. Oleh karena itu, jurnal ini membahas tentang Systematic Literature Review (SLR) pembangkitan pertanyaan pada bahasa Indonesia. SLR yang dibangun ini dapat digunakan untuk bahan pertimbangan optimalisasi penelitian tentang pembangkitan pertanyaan menggunakan bahasa Indonesia di kemudian hari. Tahapan yang dilakukan dalam pembentukan SLR adalah perencanaan literature review, kemudian melakukan literature review dan terakhir adalah pelaporan hasil literature review. Pencarian pada google scholar menghasilkan 27 penelitian yang relevan dengan kata kunci. Penerapan kriteria inklusi dan eksklusi menghasilkan 15 penelitian yang relevan. Kemudian proses backward dan forward snowballing yang dilakukan menghasilkan 2 penelitian tambahan. Total penelitian yang dianalisis berjumlah 17 penelitian. Proses selanjutnya adalah penilaian kualitas penelitian. Hasil penilaian kualitas penelitian menunjukkan bahwa keseluruhan penelitian yang berjumlah 17 penelitian tersebut memiliki kualitas yang baik untuk dianalisis. Hasil analisis yang dilakukan menunjukkan bahwa penelitian awal terkait pembangkitan pertanyaan untuk bahasa Indonesia masih memiliki beberapa celah. Diantaranya terkait dataset yang belum memadai, model pertanyaan yang kurang beragam, belum adanya penanganan/preproses model kalimat unstructured, dan belum adanya pembangkitan pertanyaan yang berasal dari gabungan beberapa informasi. AbstractResearch on automated question generation is constantly evolving. Various methods have been tried to be applied in various languages. Each language has different characteristics. Therefore, the method used to generate questions should be adapted based on the language. The automation of question generation for Indonesian has also begun to develop since 2015. The result of these studies need to be analyzed to find out the advantages and disadvantages of each method that has been used. Therefore, this journal discusses the Systematic Literature Review (SLR) for generating questions in Indonesian. The SLR that was built can be used for consideration of optimizing research on generating questions using Indonesian in the future. The steps taken in this SLR are planning analysis, then carrying out the analysis and finally reporting the analysis. A search on Google Scholar yielded 27 studies that were relevant to the keyword. The application of inclusion and exclusion criteria resulted in 15 relevant studies. Then the backward and forward snowballing processes carried out resulted in 2 additional studies. Total research analyzed amounted to 17 studies. The next process is the assessment of research quality. The results of the research quality assessment showed that the overall 17 studies had good quality for analysis. The results of the analysis carried out indicate that the initial research related to question generation for Indonesian still has some gaps. For examples about datasets, question models, handling unstructured sentence models, and generating questions from a combination of some information.
Analisis Perbandingan Algoritma Machine Learning dan Deep Learning untuk Klasifikasi Citra Sistem Isyarat Bahasa Indonesia (SIBI) Naufal, Mohammad Farid; Kusuma, Selvia Ferdiana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106823

Abstract

Terdapat orang yang tidak mampu berkomunikasi secara verbal yang menyebabkan kesulitan dalam berkomunikasi. Orang tersebut mengalami gangguan seperti tuli atau bisu. Mereka hanya dapat berkomunikasi melalui bahasa isyarat salah satunya adalah Sistem Isyarat Bahasa Indonesia (SIBI). Pengenalan Bahasa Isyarat adalah permasalahan klasifikasi yang kompleks untuk dipecahkan. Setiap bahasa isyarat memiliki sintaks dan tata bahasanya sendiri. Computer vision adalah sebuah Teknik yang digunakan komputer untuk melakukan klasifikasi citra. Computer vision membantu pengenalan citra SIBI secara otomatis sehingga memudahkan orang normal berkomunikasi dengan orang tuli atau bisu. Pada penelitian sebelumnya belum ada yang melakukan perbandingan algoritma klasifikasi machine learning dan deep learning untuk pengenalan SIBI. Perbandingan penting dilakukan untuk melihat efektifitas tiap algoritma klasifikasi dalam hal performa klasifikasi dan waktu komputasi. Algoritma klasifikasi machine learning memiliki waktu komputasi lebih rendah sedangkan Deep learning memiliki performa klasifikasi lebih tinggi. Penelitian ini menganalisis time to performance dari algoritma machine learning dan deep learning dalam melakukan klasifikasi citra SIBI huruf A hingga Z. K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional neural network (CNN) dengan transfer learning adalah tiga algorimta klasifikasi populer yang dibandingkan dalam penelitian ini. Arsitektur transfer learning yang digunakan adalah Xception, ResNet50, VGG15, dan MobileNetV2. Dari hasil penelitian yang dilakukan menggunakan 5 cross validation, CNN dengan arsitektur Xception memiliki nilai F1 Score tertinggi yaitu 99,57% dengan waktu training rata-rata 1.387 detik. Sedangkan KNN dengan nilai K = 1 memiliki waktu training tercepat yaitu 0,03 detik dan memiliki nilai F1 Score 86,95%.AbstractThe person who has a disorder such as deaf or dumb are unable to communicate verbally, which causes difficulties in communicating. They can only communicate through sign language, one of which is the Indonesian Language Sign System or Sistem Isyarat Bahasa Indonesia (SIBI). Sign Language Recognition is a complex classification problem to solve. Each sign language has its syntax and grammar. Computer vision is a technique used by computers to classify images. Computer vision helps automatically recognize SIBI images, making it easier for normal people to communicate with deaf or mute people. In previous studies, no one has compared machine learning and deep learning classification algorithms for the classification of SIBI. Therefore, a meaningful comparison is made to see each classification algorithm's effectiveness in classification performance and computation time. Machine learning classification algorithms have lower computation time, while Deep learning has higher classification performance. This study analyzes the time to performance of machine learning and deep learning algorithms in classifying SIBI images of letters A to Z. K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional neural network (CNN) with transfer learning are three popular classification algorithms compared in this study. The transfer learning architectures used are Xception, ResNet50, VGG15, and MobileNetV2. The results of research conducted using 5 cross-validation, CNN with the Xception architecture has highest F1 Score of 99.57%, with an average training time of 1.387 seconds. KNN, with a value of K = 1, has the fastest training time of 0.03 seconds and an F1 Score of 86.95%.
Transliterating Javanese Script Images to Roman Script using Convolutional Neural Network with Transfer Learning Naufal, Mohammad Farid; Siswantoro, Joko; Soebroto, Juan Timothy
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2566

Abstract

The Javanese script holds immense cultural significance within Indonesia despite its diminishing usage in contemporary contexts. Its presence remains notable in specific regions of Java and remains integral to many historical documents and texts. Consequently, there is an urgent need for a transliteration system adept at converting Javanese script into contemporary scripts like Roman or Indonesian, thereby contributing to preserving Java's linguistic and cultural legacy. However, reading or transliterating Javanese script can be time-consuming, especially for longer texts, presenting considerable challenges for non-native readers. This study aims to develop an effective transliteration system for converting Javanese script into Roman script. This system addresses the pressing need to preserve Java's linguistic and cultural heritage by facilitating the readability and accessibility of Javanese script, especially for non-native readers. This study introduces an Optical Character Recognition (OCR) system tailored to identify Javanese script characters and transcribe them into Roman characters, explicitly focusing on fundamental nglegena and sandhangan swara characters. Individual characters are isolated by leveraging horizontal and vertical projection techniques, facilitating subsequent classification using a Convolutional Neural Network (CNN) employing transfer learning methodologies. The system's achievement of an impressive average similarity score of 90.78% is noteworthy, with the Xception architecture demonstrating superior efficiency in transliteration tasks. Implementing such a system harbors significant promise in safeguarding the Javanese script and enhancing its accessibility to a broader audience. This research contributes substantially to preserving and propagating Indonesia's rich cultural and linguistic heritage amidst the digital age.
IMPLEMENTASI KLASIFIKASI SOAL BERDASARKAN TAKSONOMI BLOOM MENGGUNAKAN ALGORITMA SVM Kusuma, Selvia Ferdiana; Heriadi, Agustono; Naufal, Mohammad Farid
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 1 No. 1 (2017): PROSIDING SEMNAS INOTEK Ke-I Tahun 2017
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v1i1.427

Abstract

Proses penilaian merupakan aspek penting dalam pembelajaran. Penilaian harus dilakukan secara benar agar dapat mengukur kemampuan peserta didik. Pada soal-soal yang digunakan untuk ujian pada program Studi Teknik Informatika Politeknik Kediri belum dilakukan pengklasifikasian soal berdasar tingkat kesulitannya. Sehingga pada proses penilaian tidak didasarkan atas pemberian soal-soal dengan tingkat kesulitan yang berbeda. Pada tahun 1995, Benjamin Bloom telah memperkenalkan adanya proses pengklasifikasian soal berdasarkan tingkat kesulitannya, metode tersebut dinamakan Taksonomi Bloom. Proses pengklasifikasian soal sesuai level pada taksonomi bloom tidaklah mudah jika dilakukan secara manual. Proses otomatisasi klasifikasi perlu dilakukan ketika akan melakukan klasifikasi soal dalam jumlah yang banyak, misalkan pada proses pengklasifikasian soal pada bank soal. Otomatisasi dilakukan selain untuk mempersingkat waktu juga untuk mengurangi tendensi dari ahli pada pengklasifikasian soal. Proses klasifikasi dilakukan dengan pengidentifikasian fitur leksikal dan sintaktik sebagai proses ekstraksi fitur, kemudian hasil ekstraksi fitur diklasifikasikan menggunakan algoritma SVM. Penelitian ini menghasilkan sebuah aplikasi yang dapat melakukan pengklasifikasian sejumlah soal berdasarkan taksonomi bloom menggunakan algoritma SVM. Aplikasi ini memiliki akurasi klasifikasi soal sebesar 86%.
Co-Authors Agustono Heriadi, Agustono Alvin Eka Putra Aminulloh, Septian Wijaya Ang Alexander Yoshua Arifin, Theofilus Aryo Budilaksono Astri Agustina Arius Daniel Cristianindra R. David Sugiarto Derry Alim Fernando Fania Alya Miranti Farrel Arghya Tito Prayoga Felix Handani Ferdiana Kusuma, Selvia Jeremy Owen Lieyanto Jessica Millenia Joko Siswantoro Joseph Kristiano Juan Timothy Soebroto Kansil, Christian Nathaneil Kevin Christian Tanus Kevin Wijaya, Kevin Kusuma, Selvia Ferdiana Mirella Mercifia Mohd Sanusi Bin Azmi Muhammad Ghifari Kusuma Wicaksono Nicky Setyawan Dinata Njoto Benarkah Prasetyo, Daniel Hary Putra, Putu Aditya Riva Ramadhan, Firman Herda Raynaldy Valentino Sukiwun Renatasiva, Billy Rizka Febrina P. Selvia F Kusuma, Selvia F Selvia Ferdiana Kusuma Selvia Ferdiana Kusuma Selvia Ferdiana Kusuma Selvia Ferdiana Kusuma Sesilia Shania Sesilia Shania Soebroto, Juan Timothy Solichul Huda Solichul Huda, Solichul Stefan Axel Subrata, - Susanto, Alvin Fernando Vernolyo, Panji Yumadana Vincentius Riandaru Prasetyo Vincentius Riandaru Prasetyo Wibisono, Yudistira Rahadian Wirjawan, Hans Wisnu Aria Yustisia Yohanes Albert Lauwoto Zefanya Ardya Prayuska