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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi Perfecting a Video Game with Game Metrics Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Linguistik Komputasional Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Jurnal Pengabdian Masyarakat Indonesia eProceedings of Engineering Jurnal INFOTEL

Yuliant Sibaroni

Telkom University

Author-ID : 3730136

Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Public Health Social Sciences

Published : 103 Documents Claim Missing Document

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Prediksi Pola Penyebaran Penyakit Demam Berdarah Dengue Di Kabupaten Sukoharjo Menggunakan Metode Ordinary Block Kriging Ellisa Ratna Dewi; Sri Suryani; Yuliant Sibaroni
Indonesia Symposium on Computing Indonesian Symposium on Computing 2014/Seminar Nasional Ilmu Komputasi Teknik Informatika (SNIKTI)
Publisher : Indonesia Symposium on Computing

Show Abstract | Download Original | Original Source | Check in Google Scholar

Sistem prediksi pola penyebaran penyakit Demam Berdarah Dengue di kabupaten Sukoharjo ini dibentuk dengan meggunakan model semivariogram dan metode estimasi Ordinary Block Kriging. Model dan metode ini dipilih sebagai alat untuk memprediksi pola penyebaran penyakit di kabupaten Sukoharjo karena tidak membutuhkan informasi sebelumnya mengenai mean data, sehingga lebih mudah dalam penggunaanya. Validasi silang dipilih sebagai alat ukur validitas model agar model memiliki kriteria kelayakan untuk digunakan pada proses berikutnya. Model terbaik dihasilkan oleh Gaussian dengan validasi 0.3140, dengan variansi kriging sebesar 0.0251 pada grid 0.05. Dari grid tersebut dapat disimpulkan bahwa penyebaran terbesar berada di kecamatan Kartasura, Gatak, Baki, dan Grogol. Sedangkan error yang dihasilakan dari hasil pengujian sistem dengan membandingkan data asli populasi terjangkit dengan hasil estimasi adalah 0.158845523 atau sekitar 15%. Hasil prediksi ini mengindikasikan bahwa terdapat faktor X yang mempengaruhi penyebaran penyakit Demam Berdarah Dengue di kabupaten Sukoharjo.

Content Based Recommender System Berbasis Logika Fuzzy Dinamis Okky Brillian Hibrianto; Z K Abdurahman Baizal; Yuliant Sibaroni
Indonesia Symposium on Computing Indonesian Symposium on Computing 2014/Seminar Nasional Ilmu Komputasi Teknik Informatika (SNIKTI)
Publisher : Indonesia Symposium on Computing

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Perkembangan teknologi menyebabkan makin maraknya jual beli online serta cepat bergantinya fitur-fitur dari suatu produk elektronik. Akan tetapi banyak calon pembeli bingung jika hanya melihat deretan spesifikasi pada suatu produk berspesifikasi teknis,karena itu dibutuhkan suatu variabel-variabel yang menampung keinginan user secara lebih general dan bersifat fungsional untuk merekomendasikan produk elektronik serta mengeluarkan hasil rekomendasi yang tepat sesuai perkembangan teknologi yang terjadi . Content Based Recommender system dengan logika fuzzy dinamis merupakan salahsatu pendekatan yang bisa dilakukan untuk merekomendasikan produk elektronik untukÂ seorang user sesuai dengan preferensi fungsional tertentu berdasarkan konten spesifikasi asli dari produk yang dapat memproses nilai-nilai batasan dalam fitur yang berbeda menjadi satu nilai derajat kepuasan untuk memenuhinya yang sesuai perkembangan zaman. Hasil dari percobaan dengan kasus rekomendasi smartphonepada penelitian ini menunjukkan bahwa sistem dapat merekomendasikan produk smartphone sesuai dengan preferensi yang dimasukkan oleh user dengan nilai rata-rata precision sebesar 0,993 dan dengan nilai error perangkingan rata-rata sebesar 0,482.

Simulasi Pengaturan Lampu Lalu Lintas Menggunakan Cellular Automata dan Fuzzy Inference System Septian Nugraha Kudrat; Yuliant Sibaroni; Erwin Budi Setiawan
Indonesia Symposium on Computing Indonesia Symposium on Computing 2015
Publisher : Indonesia Symposium on Computing

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Permasalahan kemacetan tidak mudah diatasi karena pertumbuhan populasi kendaraan bertambah. Salah satu efek pertumbuhan populasi kendaraan adalah menjadi sensitifnya pengaturan traffic light pada suatu persimpangan. Sistem pengaturan traffic light yang tidak sesuai dengan keadaan jumlah kendaraan dapat memicu kemacetan. Pada umumnya, sistem pengaturan traffic light menggunakan pengaturan fixed time. Pengaturan fixed time tidak menyesuaikan dengan keadaan jumlah kendaraan sehingga tundaan yang dihasilkan berpotensi lama. Dikembangkan skema pengaturan adaptif menggunakan Fuzzy Inference System (FIS). FIS menghasilkan durasi lampu hijau. FIS tidak memiliki parameter performa untuk menguji kemampuannya sehingga sistem perlu diintegrasikan dengan model Cellular Automata (CA). Pergerakan kendaraan yang dihasilkan CA dapat memunculkan tundaan dan kecepatan rata-rata. Indikator keberhasilan didasarkan pada waktu tunggu rata-rata dan kecepatan rata-rata yang dialami oleh setiap kendaraan dalam waktu pengamatan per time step. Selain itu, pembagian durasi lampu lalu lintas dalam satu siklus menjadi faktor pertimbangan tambahan untuk menganalisis performa sistem. Metode FIS menghasilkan hingga 76,2 % tundaan rata-rata pada kelas E dan menghasilkan hingga 23,8 % tundaan rata-rata pada kelas F. Skema Fixed Time menghasilkan 0 % tundaan rata-rata pada kelas E dan 100 % tundaan rata-rata pada kelas F sehingga tundaan yang dihasilkan skema Fixed Time lebih lama daripada tundaan yang dihasilkan metode FIS. Â

EFFECTIVENESS OF SVM METHOD BY NAÃ¯VE BAYES WEIGHTING IN MOVIE REVIEW CLASSIFICATION Zain, Fadli Fauzi; Sibaroni, Yuliant
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.7770

Classification of movie review belongs to the realm of text classification, especially in the field of sentiment analysis. One familiar text classification method used is support vector maching (SVM) and Naïve Bayes. Both of these methods are known to have good performance in handling text classification separately. Combining these two methods is expected to improve the performance of classifier compared to working separately. This paper reports the effort to classify movie reviews using the combined method of Naïve Bayes and SVM with Naïve Bayes as weights. This combined method is commonly called NBSVM. The results showed the best accuracy is obtained if the classification is done by the NBSVM method, which is equal to 88.8% with the combined features of unigram and bigram and using pre-processing in the form of data cleansing only.

Deteksi Kanker Berdasarkan Klasifikasi Data Microarray Menggunakan Least Absolute Shrinkage and Selection Operator dan Functional Link Neural Network Putri, Dinda Rahma; Adiwijaya, Adiwijaya; Sibaroni, Yuliant
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i4.2349

Cancer is a dangerous disease that arises from the conversion of normal cells into tumor cells that develop into malignant tumors. According to WHO, cancer is the second deadliest disease in the world. About 70% of cancer deaths occur in low and middle income countries such as Indonesia. Cancer can be detected by recognizing patterns of expression of human genes. DNA Microarray is a technology that can find patterns of gene expression in a variety of different conditions by means of microarray data classification. Microarray data has very large dimensions and needs to be reduced in order to obtain informative genes to detect cancer optimally. In this study, the authors use the Least Absolute Shrinkage and Selection Operator (LASSO) as a feature selection method to reduce data dimensions and Functional Link Neural Network (FLNN) as a classification method with Legendre Polynomial base functions. With a series of processes that have been carried out, obtained an average accuracy of 86.41% and an average f1-score of 81.83%

Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes Rachmadania Irmanita; Sri Suryani Prasetiyowati; Yuliant Sibaroni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Malaria is a disease caused by the Plasmodium parasite that transmitted by female Anopheles mosquitoes. Malaria can become a dangerous disease if late have the medical treatment. The late medical treatment happened because of misdiagnosis and lack of medical staff, especially in the countryside. This problem can cause severe malaria that has complications. This study creates a system prediction to classify the severe malaria disease using Classification and Regression Tree (CART) method and the probability of malaria complication using Naïve Bayes method. The first step of this study is classifying the patients that have symptom are infected severe malaria or not based on the model that has been built. The next step, if the patient classified severe malaria then the data predicted if there any probability of complication by the malaria. There are 8 possibilities of complication malaria which are convulsion, hypoglycemia, hyperpyrexia, and the combinations of these four. The first step will evaluate by using F-score, precision and recall while the second step will evaluate by using accuracy. The highest result F-score, precision and recall are 0.551, 0.471 and 0.717. The highest accuracy 81.2% which predicted the complication is Hypoglycemia.

Sentiment Analysis of Public Opinion Related to Rapid Test Using LDA Method Viny Gilang Ramadhan; Yuliant Sibaroni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

In 2020 the world will be shocked by an outbreak of a disease that has developed tremendously. This disease is the Coronavirus. The Indonesian government, in overcoming conducted a Rapid early detection test in the spread of the Coronavirus. The steps of the Indonesian government have received rejection in several areas because people consume hoax news on social media. Indonesians widely use Twitter in conversations about the Coronavirus. Previous research was carried out using large-scale data, which affected the performance of the topic extraction method. The classification used resulted in poor accuracy using LDA to find the probability of topics in existing documents. LDA excels in large-scale data processing and is more consistent in generating the topic proportion value and word probability. Aspect-based sentiment analysis on public opinion regarding the rapid test on Twitter using LDA can determine aspects and public opinion on the rapid test. The test results of this study obtained 7000 tweets, four aspects of the results of topic using LDA, and getting the best accuracy using the RBF kernel by 95%. The sentiment of the Indonesian people towards the Rapid test is positive, with 4,305 sentiments.

Classification of Dengue Hemorrhagic Fever (DHF) Spread in Bandung using Hybrid NaÃ¯ve Bayes, K-Nearest Neighbor, and Artificial Neural Network Methods Fatri Nurul Inayah; Sri Suryani Prasetiyowati; Yuliant Sibaroni
International Journal on Information and Communication Technology (IJoICT) Vol. 7 No. 1 (2021): June 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v7i1.562

Dengue fever is a dangerous disease caused by the dengue virus. One of the factors causing dengue fever is due to the place where you live in the tropics, so that cases of dengue fever in Indonesia, especially in the Bandung Regency area, will continue to show high numbers. Therefore, information is needed on the spread of this disease by requiring the accuracy and speed of diagnosis as early prevention. In terms of compiling this information, classification techniques can be done using a combination of methods NaÃ¯ve Bayes, K-Nearest Neighbor(KNN), and Artificial Neural Network(ANN) to build predictions of the classification of dengue fever, and the data used in this Final Project are dataset affected by the spread of dengue fever in Bandung regency in the 2012-2018 period. The hybrid classifier results can improve accuracy with the voting method with an accuracy level of 90% in the classification of dengue fever.

Sentiment Analysis Terhadap Tweet Bernada Sarkasme Berbahasa Indonesia Lanny Septiani; Yuliant Sibaroni
Jurnal Linguistik Komputasional Vol 2 No 2 (2019): Vol. 2, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Sarkasme dapat mengubah polaritas kalimat dari positif atau negatif menjadi sebaliknya. Sementara senti-men analisis pada sosial media sudah banyak dimanfaatkan, tetapi masih jarang sekali ditemukan sentimen analisis yang mempertimbangkan pendeteksian sarkasme didalamnya. Hal ini tentu akan mempengaruhi kualitas dari hasil analisis. Percobaan mengenai sentimen analisis dengan pendeteksian sarkasme lebih sering ditemukan pada penggunaan bahasa Inggris. Oleh karena itu, dengan mengacu pada penelitian yang dilakukan pada tweet berbahasa Inggris, pada penelitian ini kami menganalisa sentimen analisis bernada sarkasme pada Tweet berbahasa Indonesia dengan menggunakan fitur interjeksi dan unigram sebagai fitur utama oendeteksi kalimat sarkasme serta membandingkan 2 metode klasifikasi yaitu Naive Bayes dan Support Vector Machine dengan kernel polinomial. Fitur interjeksi menyatakan fitur yang memuat kata-kata yang mengungkapkan perasaan dan maksud seseorang, sedangkan fitur unigram merupakan kumpulan kata tunggal yang diperoleh dari korpus secara otomatis. Hasil eksperimen menunjukkan penggunaan fitur interjeksi dan unigram sebagai pendeteksian sarkasme pada tweet berbahasa Indonesia mampu meningkatkan akurasi dengan rata-rata kenaikan akurasi lebih dari 8% untuk classifier Naive Bayes dan lebih dari 13% untuk classifier Support Vector Machine dibandingkan hanya menggunakan fitur unigram saja. Hasil lainnya adalah akurasi terbaik adalah metode Naive Bayes dengan akurasi terbaik yang diperoleh mencapai lebih dari 91.

The Prediction of Optimal Route of City Transportation Based on Passenger Occupancy using Genetic Algorithm: A Case Study in The City of Bandung Sri Suryani Prasetiyowati; Yuliant Sibaroni; Derwin Prabangkara
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.7077

Currently, the existence of city transport is increasingly eliminated by private vehicles such as cars and motorcycles. This situation is further exacerbated by the behavior of city transport drivers who are less discipline in driving, or in picking up and dropping off their passengers. The bad behavior is partly caused by the low level of passenger occupancy. The drivers try to search for passengers as much as possible but often ignore the traffic rules. To overcome this problem, an optimal transport route with high passenger potential is required. Therefore, this study investigated the optimal route of city transport based on the passenger occupancy rate in the city of Bandung as the case study. The method employed for determining the optimal route is Genetic algorithm combined with Ordinary Kriging method used for the process of passenger prediction and fitness calculation. The optimal routes are those with higher occupancy rate. The analysis results showed that the use of the Genetic algorithm with a low number of generations succeed in creating new optimal routes even though the increase is not too high the maximum only reaches 4%.This result is certainly important enough to be used in making better public transport routes.

Co-Authors Abduh Salam Adhe Akram Azhari Aditya Andar Rahim Aditya Firman Ihsan Aditya Gumilar Aditya Iftikar Riaddy Adiwijaya Agi Maulana Alam Rizki Fitriansyah Anak Agung Istri Arinta Maharani Andrew Wilson Annisa Aditsania Arya Pratama Anugerah Attala Rafid Abelard Aulia Rayhan Syaifullah Azmi Aulia Rahman Benaya, Raisa Bunga Sari Chamadani Faisal Amri Chindy Amalia Cika Carissa Sujadi Claudia Mei Serin Sitio Damarsari Cahyo Wilogo Delvanita Sri Wahyuni Derwin Prabangkara Desianto Abdillah Devi Ayu Peramesti Dhina Nur Fitriana Dhina Nur Fitriana Diki Wahyudi Diyas Puspandari Dufha Arista Elita Aurora Az Zahra Ellisa Ratna Dewi Ellisa Ratna Dewi Elqi Ashok Erwin Budi Setiawan Fadhilah Nadia Puteri Fadli Fauzi Zain Faiza Aulia Rahma Putra Fatihah Rahmadayana Fatri Nurul Inayah Fauzaan Rakan Tama Feby Ali Dzuhri Fery Ardiansyah Effendi Ferzi Samal Yerzi Fhira Nhita Fitriyani F. Fitriyani Fitriyani Gilang Brilians Firmanesha Hanvito Michael Lee I Gusti Ayu Putu Sintha Deviya Yuliani I Putu Ananda Miarta Utama Ibnu Muzakky M. Noor Iklima Apriani Indra Kusuma Yoga Indwiarti, Indwiarti irbah salsabila Irfani Adri Maulana Irma Palupi Izzan Faikar Ramadhy Izzatul Ummah, Izzatul Janu Akrama Wardhana Kemas L Muslim Lanny Septiani Laura Imanuela Mustamu Lintang Aryasatya Lisbeth Evalina Siahaan Livia Naura Aqilla Mahmud Imrona Mega Vebika Shyahrin Mitha Putrianty Fairuz Muhammad Abdurrahman Al Jauzy Muhammad Ammar Fathin Muhammad Arif Kurniawan Muhammad Damar Muhammad Ghifari Adrian Muhammad Hadyan Baqi Muhammad Ikram Kaer Sinapoy Muhammad Kiko Aulia Reiki Muhammad Rajih Abiyyu Musa Muhammad Sulthon Asramanggala Nanda Ihwani Saputri Naufal Alvin Chandrasa Ni Made Dwipadini Puspitarini Niken Dwi Wahyu Cahyani Nuraena Ramdani Okky Brillian Hibrianto Okky Brillian Hibrianto Pernanda Arya Bhagaskara S M Prawiro Weninggalih Priyan Fadhil Supriyadi Putri, Dinda Rahma Rachmadania Irmanita Rafik Khairul Amin Rafika Salis Revi Chandra Riana Rian Febrian Umbara Rian Putra Mantovani Ridha Novia Ridho Isral Essa Rifki Alfian Abdi Malik Riski Hamonangan Simanjuntak Rizki Annas Sholehat Rizki Nabil Aufa Rizky Fauzi Ramadhani Rizky Yudha Pratama Saniyah Nabila Fikriyah Septian Nugraha Kudrat Septian Nugraha Kudrat Siti Inayah Putri Siti Uswah Hasanah Sri Suryani Prasetiyowati Sri Suryani Prasetiyowati Sri Suryani Prasetiyowati Sri Suryani Prasetiyowati Sri Suryani Prasetiyowati Sri Suryani Prasetyo Sri Suryani Prasetyowati Sri Suryani Sri Suryani Titan Kinan Salaatsa Viny Gilang Ramadhan Widya Pratiwi Ali Wikantari, Made Mita Winico Fazry Wira Abner Sigalingging Zain, Fadli Fauzi ZK Abdurahman Baizal

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