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All Journal TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal on Computing (Indo-JC) Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA JTP - Jurnal Teknologi Pendidikan Building of Informatics, Technology and Science Jurnal Locus Penelitian dan Pengabdian eProceedings of Engineering Advance Sustainable Science, Engineering and Technology (ASSET)
Dade Nurjanah
Universitas Telkom
Aerospace Engineering Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Diagnosa Gejala Penyakit Demam Berdarah Dan Malaria Berbasis Ontologi Odia Pratama; Dade Nurjanah
eProceedings of Engineering Vol 6, No 2 (2019): Agustus 2019
Publisher : eProceedings of Engineering

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Abstract

Kementerian Kesehatan Indonesia menyatakan bahwa prioritas penanganan penyakit menular yangdiakibatkan oleh gigitan nyamuk tertuju pada penyakit DBD dan Malaria. Kedua penyakit tersebutmemiliki gejala penyakit yang mirip sehingga tidak jarang pasien di suatu rumah sakit meninggal duniaakibat kesalahan diagnosa penyakit tersebut. Oleh karena itu, dibutuhkan suatu sistem berbasispengetahuan dalam bentuk ontologi gejala penyakit DBD dan Malaria serta menggunakan metodeCertainty Factor untuk mengatasi faktor ketidakpastian pada ontologi. Tujuannya adalah untuk membantudiagnosa pasien yang terjangkit penyakit akibat gigitan nyamuk tersebut. Pada tugas akhir ini dibangunsebuah model ontologi dari penyakit DBD dan malaria. Ontologi tersebut bertujuan untuk membantudokter dalam mendiagnosa gejala penyakit DBD dan malaria dan memberikan informasi penting seputargejala dari penyakit DBD dan malaria. Kata kunci : Ontologi, DBD, MalariaAbstract The Ministry of Healthcare of the Republic of Indonesia said that handling infectious diseases caused bymosquito bites, which are dengue and malaria, is placed as the highest priority. Both diseases havesimilar symptoms that sometimes cause fatality, due to misdiagnosis. Therefore, an ontology-based tool isrequired to model the symptoms of these disease. The purpose the tool is to assist doctor and patient inearly diagnosis of the diseases. In this final project, an ontology model of dengue and malaria has beenbuilt. It aims to help doctors diagnose dengue and malaria and provide important information about dengueand malaria. Experiments with various symptoms have given accurate results whether Dengue or Malaria. Keywords: Ontology, DHF, Malaria
Sistem Rekomendasi Mata Kuliah Pilihan Menggunakan Association Rule Dan Ant Colony Optimization (studi Kasus Mata Kuliah Di Jurusan Teknik Informatika Universitas Telkom) Aditia Rafif Khoerulloh; Dade Nurjanah; Ade Romadhony
eProceedings of Engineering Vol 6, No 2 (2019): Agustus 2019
Publisher : eProceedings of Engineering

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Abstract

AbstrakDalam proses perkuliahan, setiap mahasiswa diwajibkan untuk menyelesaikan setiap mata kuliah dengansejumlah sks yang ditentukan oleh institusi. Ada mata kuliah yang bersifat wajib dan pilihan. Mata kuliahpilihan berdampak pada fokus bidang penelitian yang akan diambil. Pemilihan hal tersebut membantudalam pengerjaan tugas akhir yang lebih baik. Pemilihan mata kuliah yang tidak sesuai dengan riwayat nilai dan ekspektasi mahasiswa bisa menyebabkan kesulitan dalam penyelesaian tugas akhir. Padapenelitian ini akan dirancang sistem rekomendasi mata kuliah pilihan menggunakan metode brute force,association rule dan metode ant colony optimization (ACO). Metode brute force digunakan untukmendapatkan rekomendasi mata kuliah pilihan berdasarkan riwayat nilai. Algoritma apriori padametode association rule digunakan untuk menemukan asosiasi setiap mata kuliah pilihan. Sedangkanalgoritma ant cylce pada metode ACO digunakan untuk mengoptimasi hasil pencarian rule sebagairekomendasi learning path pengambilan mata kuliah pilihan. Kemudian hasil dari ketiga algoritmatersebut digabungkan untuk hasil rekomendasi yang lebih baik. Hasil penelitian menunjukan bahwaalgoritma brute force dapat memberikan rekomendasi sesuai riwayat nilai. Algoritm apriori pada metodeassociation rule dapat menghasilkan rule untuk setiap kelompok keahlian dan algoritma ant cycle padaalgoritma ACO dapat memberikan rekomendasi berupa rule pemilihan mata kuliah pilihan dan learningpath mata kuliah pilihan. Hasil penggabungan tiga algoritma dapat menghasilkan rekomendasi matakuliah pilihan dengan baik.Kata kunci: course recommendation, association rule, ant colony optimization (ACO), apriori, ant cycle, brute force AbstractIn the lecture process, each student is required to complete each course with several credits determined by the institution. Some courses are mandatory and optional. Elective courses have an impact on thefocus of the research area to be taken. The selection of these things helps in the completion of the finalproject. Selection of courses that are not by the history of grades and expectations of students can causedifficulties in completing the final project. In this study, a recommendation subject system will bedesigned using the brute force method, association rule, and the ant colony optimization (ACO) method.The brute force method is used to obtain elective course recommendations based on historical grades. Apriori algorithm in the association rule method is used to find the association of each chosen subject.While the ant cycle algorithm in the ACO method is used to optimize the rule search results as arecommendation for learning path taking elective courses. Then the results of the three algorithms arecombined for better recommendation results. The results showed that the brute force algorithm canprovide recommendations according to historical values. A priori algorithm in the association rulemethod can produce rules for each group of expertise and the ant cycle algorithm in the ACO algorithmcan provide recommendations in the form of rules for selecting the subject and learning path for selectedsubjects. The results of combining the three algorithms can produce a choice of elective courses well.Keywords: course recommendation, association rule, ant colony optimization (ACO), apriori, ant cycle,brute force
Tourism Recommendation System using Weighted Hybrid Method in Bali Island Diffo Elza Pratama; Dade Nurjanah; Hani Nurrahmi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6409

Abstract

Tourism is a promising sector for global economic growth, as it has shown resilience during the global crisis. In Bali, tourism is a leading sector alongside agriculture and industry, making a significant contribution to regional and community development. However, Bali's popularity as a sought-after tourist destination also raises the need for an information system that can provide destination recommendations. To overcome the problem of information overload, a recommendation system is needed. This study tested the tourism recommendation system in Bali using the Weighted Hybrid technique which combines two methods, namely Collaborative Filtering and Content-Based using the weighted value technique. Collaborative Filtering, Content-Based, and Weighted Hybrid approaches will be compared in this study to improve the performance and accuracy of current recommendation systems. Utilizing the MAE, MSE, and RMSE values, the evaluation is carried out by comparing the evaluation matrices of the three Collaborative Filtering, Content-Based, and Weighted Hybrid methods. With MAE, MSE, and RMSE values of 0.4854, 0.4034, and 0.6351 respectively, the evaluation findings show that the Weighted Hybrid technique beats Collaborative Filtering and Content-Based with a weight value of 0.4.
Music Recommendation System Using Alternating Least Squares Method Muhammad Rafi Irfansyah; Dade Nurjanah; Hani Nurrahmi
Indonesia Journal on Computing (Indo-JC) Vol. 9 No. 1 (2024): April, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.1.908

Abstract

Music is not just entertainment, but it also has a positive impact on psychological well-being. The music landscape is generally dominated by millennials, especially in Indonesia. Music recommendation systems are becoming an important factor in offering songs that match users' preferences. Collaborative Filtering (CF), particularly the Alternating Least Squares (ALS) method, has become a popular solution for data sparsity problems in user-item interactions. Using the Precision@K metric, ALS provides the best results at a 50:50 data split ratio, 0.30225 for the Last FM dataset and 0.19742 for the Taste Profile dataset. Further analysis shows that ALS is more effective on datasets with balanced data distributions, such as Last FM, than on datasets with noisier characteristics, such as Taste Profile. The main conclusion is that ALS is suitable for use on datasets with balanced data distributions and can provide more optimal recommendations. For further development, handling sparsity data on Taste Profile needs to be improved to improve the performance of the recommendation model. This illustrates the importance of adapting the model to the unique characteristics of each dataset to achieve more accurate music recommendations.
Cyberbullying Detection on Twitter using Support Vector Machine Classification Method Putri Waisnawa, Ni Luh Putu Mawar Silveria; Nurjanah, Dade; Nurrahmi, Hani
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (398.518 KB) | DOI: 10.47065/bits.v3i4.1435

Abstract

Bullying is when someone or a group of individuals is continuously attacked. Because of the advancement of the internet, it has become very easy for society to engage in harmful acts of bullying by attacking a person or group of people who can hurt the victim, this is known as cyberbullying. Twitter is a social media platform that may be used by the society to share information and can also be used to perpetrate cyberbullying actions by sending messages (tweets) that addressed to the victims. This final project was developing a system to detect cyberbullying on Twitter. The system uses the Support Vector Machine method to classify whether the tweets that are shared include cyberbullying or not. In addition, this research also uses Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram feature extraction for data that has gone through the pre-processing stage. In collecting data, the author crawled tweets based on the keywords 'jelek', 'bodoh', 'goblok', 'brengsek', 'bangsat', 'memalukan', 'laknat', 'bacot' and 'pelacur'. The best performance results of the research is 76.2% accuracy, 73.2% precision, 78.2% recall and 75.6% F1-Score generated by the RBF kernel with a total of n=1
Recommendation System from Microsoft News Data using TF-IDF and Cosine Similarity Methods Yunanda, Gisela; Nurjanah, Dade; Meliana, Selly
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.563 KB) | DOI: 10.47065/bits.v4i1.1670

Abstract

The rapidly growing information causes information overload, so news portals publish information massively. Readers need time to search and read more news, but the time relevance of news wears off quickly. A recommendation system is needed that can recommend news according to the preferences of readers. This study recommends news using the TF-IDF method. TF-IDF gives weight to each word in the news title, and then looks for similarity between stories using cosine similarity. To prove the accuracy of whether the system recommendation results were actually clicked by the reader, the recommendation results were matched with the reader's news history on the online news portal Microsoft News using a hit-rate. The hit-rate result in this study was 80.77%.
Hate Speech Detection on Twitter through Natural Language Processing using LSTM Model Arbaatun, Cepthari Ningtyas; Nurjanah, Dade; Nurrahmi, Hani
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2718

Abstract

Currently, social media is a place to express opinions. This opinion can be positive or negative. However, lately, the opinion that often appears is a negative opinion, such as hate speech. Hate speech is often found on social media, such as malicious comments intended to insult individuals or groups. Based on WeAreSocial data in 2021, one of the most used social media platforms in Indonesia is Twitter, with 63.6% of users. According to the Indonesia National Police, hate speech cases were more dominant during the period from April 2020 to July 2021. Therefore, efforts are needed to identify hate speech on the Twitter platform. One way to detect hate speech is by using deep learning. In this research, we use a deep learning model of Long Short-Term Memory (LSTM) with word embedding. FastText and Global Vector (GloVe) is the word embeddings that we use as input for word representation and classification. FastText embeddings make use of subword information to create word embeddings and GloVe embeddings using an unsupervised learning method trained on a corpus to generate distributional feature vectors. From the evaluation results on the experimental model, LSTM-FastText using random oversampling has an advantage with an F1-score of 89.91% compared to LSTM-GloVe to obtain an F1-score of 82.14%.
Utilizing Sequential Pattern Mining and Complex Network Analysis for Enhanced Earthquake Prediction Henri Tantyoko; Dade Nurjanah; Yanti Rusmawati
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.1003

Abstract

Earthquakes are natural events caused by the movement of the earth's plates, often triggered by the energy release from hot liquid magma. Predicting earthquakes is crucial for raising public awareness and preparedness in seismically active areas. This study aims to predict earthquake activity by identifying patterns in seismic events using Sequential Pattern Mining (SPM). To enhance the prediction accuracy, Sequential Rule Mining (SRM) is applied to derive rules with confidence values from these patterns. The results show that using betweenness centrality as a weight increases the prediction accuracy to 83.940%, compared to 78.625% without weights. Using eigenvector centrality as a weight yields an accuracy of 83.605%. These findings highlight the potential of using centrality measures to improve earthquake prediction systems, offering valuable insights for disaster preparedness and risk mitigation.
Diversity Balancing in Two-Stage Collaborative Filtering for Book Recommendation Systems Rifqi Fauzia Muttaqien; Dade Nurjanah; Hani Nurrahmi
JURNAL TEKNIK INFORMATIKA Vol 16, No 2 (2023): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v16i2.36580

Abstract

A book recommender system is a system used to provide relevant book recommendations for readers. One approach that is often used in recommender systems is Collaborative Filtering (CF). CF provides book recommendations based on books liked by other similar users. However, CF only provides recommendations for items that are popular, so items that are less popular will be difficult to recommend. Therefore, we propose a book recommendation system based on Two-stages CF using the Diversity Balancing method. Diversity Balancing method in CF is used to balance diversity in the recommendation results by replacing popular items with less popular relevant items. System accuracy is measured using precision and recall, while diversity is measured using personal diversity and aggregate diversity. The test results show that the accuracy of the proposed system increases with the increasing number of recommended items. meanwhile, the diversity of recommended items continues to decrease as more items are included in the recommendation list. In consideration of the trade-off between accuracy and diversity, our system achieves a recall score of 0.301, a precision score of 0.282, a PD score of 0.048, and an AD score of 0.095 with a recommendation list size of 8 items.
Educational Game for Computational Thinking Ismail, Asep Maulana; Nurjanah , Dade
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 10 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i10.4878

Abstract

Computational Thinking (CT) is a fundamental problem-solving skill that encompasses analytical abilities to generate effective and efficient solutions. The core components of CT include decomposition, pattern recognition, abstraction, algorithms, and evaluation/debugging, with abstraction and decomposition serving as crucial foundational skills. The challenge of teaching CT in Indonesian schools stems from the shortage of teachers with adequate informatics competence, making innovative approaches such as necessary educative games. This study aims to develop and examine the effectiveness of an educational game based on a modified LightBot concept to train abstraction and decomposition skills among high school students who have never received CT instruction. The research method involved game design, implementation, and experimentation using a pre-test and post-test design. Data were analyzed descriptively to compare the improvement in CT skills between the experimental and control groups. The results showed a significant increase in CT scores, particularly in the abstraction and decomposition components, for the group using the educational game. Furthermore, the motivation survey indicated improved learning engagement in the experimental group. These findings strengthen the potential of educative games as an effective interactive learning medium for developing CT skills at the high school level.
Co-Authors Ade Romadhony Aditia Rafif Khoerulloh Aditiyan Iswahyudin Adiyaka Niastya Ihsan Maulana Agung Toto Wibowo Arbaatun, Cepthari Ningtyas Ardiyanto, Fajar Widhi Ayu, Ravika Berliana Shafa Wardani Dhuki Dwi Rachman Diffo Elza Pratama Fahmi, Fitra Zul Fakhri Fauzan Fikri Bahiransyah Fitra Zul Fahmi Florita Diana Sari Hani Nurrahmi Henri Tantyoko Henri Tantyoko Hilmi Eko Arianto Irfansyah, Muhammad Rafi Ismail, Asep Maulana Jayaperwira, Iklil Johan NMP Simatupang Josua Deston Girsang Julia Julia Maman Abdurohman Mohammad Fathurrahman Muhammad Rafi Irfansyah Muttaqien, Rifqi Fauzia Odia Pratama Putri Waisnawa, Ni Luh Putu Mawar Silveria R. Hatim Muhammad Amin Rahmawati Sitti Azizah Razaq, Muhammad Thaariq Rifqi Fauzia Muttaqien Riki Nur Afifuddin Rita Rismala Rusmawati, Yanti sa'adah, siti Sari Rahmawati Selly Meliana Setyadi Putra Rahmandya Shendy Murty Siti Sa'adah Wahyu Setiawan Wardani, Berliana Shafa Yanti Rusmawati Yunanda, Gisela Zaki Mudzakir Hidayatullah