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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal POINTER Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Systemic: Information System and Informatics Journal Community Empowerment
Putra Pandu Adikara
Universitas Brawijaya
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

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Klasifikasi Penyakit Kanker Serviks dengan Extreme Learning Machine Uke Rahma Hidayah; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cervical cancer is the second most common cancer in Indonesia after breast cancer. The number of deaths from cervical cancer in Indonesia continues to increase every year due to delays in making diagnoses and examinations. To detect cervical cancer, a laboratory examination using Visual Inspection with Acetic Acid (IVA) or pap smears is needed which requires specialist internal medicine and several considerations of features to get accurate diagnosis. Sometimes, how to analyze features by doctor with one another produces different results. Therefore, a classification process is needed to make a diagnosis of cervical cancer with high accuracy results so that it is expected to be able to match the diagnosis results of medical personnel. This study uses cervical cancer risk classification data with feature selection based on expert interviews. This study uses the Extreme Learning Machine algorithm to carry out the classification process and measure the results of algorithm performance with accuracy values ​​from the calculation of confusion matrix. Based on the test results obtained the optimal parameters are as many as 11 hidden neurons, the activation function is binary sigmoid, and the fold on training and testing data is fold 1st which produces an accuracy of 91.76%.
Analisis Sentimen Pada Ulasan Aplikasi Mobile Banking Menggunakan Metode Support Vector Machine dan Lexicon Based Features Katherine Ivana Ruslim; Putra Pandu Adikara; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sentiment analysis is a very popular field of research in text mining. The basic idea of ​​sentiment analysis is finding the polarity of the document and classifying it into positive or negative. The text documents used in the research are reviews on the Google Play Store regarding the mobile banking application. Support Vector Machine is a method used and added Lexicon Based Features as additional feature besides using the Bag of Words. The research data is 500 data by dividing 90% training data and 10% test data. The system evaluation results obtained with a combination of Bag of Words and Lexicon Based Features are higher than the results of system evaluations that only use the Bag of Words and systems that only use Lexicon Based Features. The evaluation results obtained by the combination of the two features with testing using 10 fold cross validation are accuracy = 0,846, recall = 0,846, precision = 0,864, and f-measure = 0,855 with the Support Vector Machine parameter value used is the best parameter value of sigma kernel RBF = 3, lambda = 0,1, gamma = 0,001, complexity = 0,1, epsilon = 0,001, and iteration = 50.
Klasifikasi Komentar Body Shaming Beauty Vlogger Pada Youtube Menggunakan Metode BM25 dan K-Nearest Neighbor Pengkuh Aditya Prana; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Beauty vlogger is a term for people who do vlog activities to discuss beauty issues and make up tutorials on YouTube. Beauty vloggers often get body shaming comments. In Indonesia, body shaming comments are a violation regulated in the Electronic Information and Transaction Act (UU ITE). Body shaming comment classification system can help to classify body shaming comments more efficient and faster. Body shaming comment classification system in this research uses the BM25 and K-Nearest Neighbor methods. Process in this research are pre-processing each data to look for words that are characteristic for each data, then calculate the term frequency based on the number of words contained in each data, then calculate the inverse document frequency, then calculate the BM25 score and sorting the data. The last step is to do the K-Nearest Neighbor classification. This study uses 600 data comments with 300 data on body shaming class, and 300 data on not body shaming class. The average of all k-fold cross validation tests obtained the highest value, namely precision = 0.87153019, recall = 0.86666667, f-measure = 0.86606885, and accuracy = 0.86666667 at value k = 3. The value of testing using balanced data is much better than testing using unbalanced data, with the highest average value of testing unbalanced data, namely precision = 0.84306693, recall = 0.775, f-measure = 0.7582337, and accuracy = 0.775.
Analisis Sentimen terhadap Ulasan Hotel menggunakan Boosting Weighted Extreme Learning Machine Riza Cahyani; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Along with the increasing competition in hotel business, every hotel tries to improve their quality for increasing their profits. Hotel can improve their quality by understanding hotel reviews that written on the internet. However, the variety of types of review made hotels difficult to analyze the type of sentiment on review. In addition, the distribution of sentiment types in the reviews was unbalanced. Therefore, analysis sentiment is carried out to determine the sentiment of hotel reviews easily. The method that used by researcher is Boosting Weighted ELM because this method can handle unbalanced class. Sentiment analysis determine by doing some pre-processing, term weighting, normalization, and classification. Testing process were carried out using k-fold cross validation with k is 5. Data that used were 500 data consisting 343 positive class and 157 negative class. Testing result shows that the model is produced with the highest f-measure value is 0,953. Optimal value of each parameter are C =16, L = 64 and weak learner = 256.
Klasifikasi Teks Pengaduan Suara Warga Kabupaten Pasuruan menggunakan Metode Maximum Entropy Mayang Panca Rini; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Suara Warga is a website that it provided by the government of Pasuruan Residence to receive complain from the society. In the application, the admin must pass on the complain to related official manually. To increase the efficient of time, it is needed a classification of the text, Maximum Entropy is a method that is used in this research with Confusion Matrix evaluation method which will count the evaluation from the equal-wont data and the unequal-wont data, with the complain as much 200 data. Before doing the classification, the first step which is done is pre-processing and the next is process of word quality. Classification is done through looking for the opportunity of every word in every document and the result of classification is got based on the higher opportunity result from document class. The result of equal-wont data evaluation produce better result than the result of the unequal-wont data evaluation with the accuration: 89,27%, precision: 92,49%, recall: 89,27% and f-measure: 89,44%.
Rekomendasi Lagu berdasarkan Lirik dan Genre Lagu menggunakan Metode Word Embedding (Word2Vec) Melati Ayuning Lestari; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Listening to songs has become a norm in society, serving many different purposes, and songs are released frequently nowadays, especially by media-service providers. Users need to overcome the struggle of selecting specific songs because of the enormous information provided by media-service providers. The song recommendation model can play an important part in this puzzlement as an automatic song selector, thus improving the user's experience. In this research, the song recommendation model uses Word2Vec Skip-Gram that functions as a query expansion for the sole purpose of finding the desired lyrics by producing a weight for query expansion. TF-IDF is first used to select the words in the lyrics that will be expanded. The model will give a list of 10 recommended songs. The evaluation results of the recommended song list shows the highest average of precision@10 score of 0.584 and the highest Mean Average Score (MAP) score of 0.7278.
Klasifikasi Hoaks Berbahasa Inggris menggunakan Boosting Weighted Extreme Learning Machine Luthfi Mahendra; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rapid technological developments have caused hoax to be more easily disseminated through the internet, especially for politic related news. Although it looks trivial, hoax can cause various kinds of problems such as community riots and the blocking of social media sites. To overcome the problems that can be caused by hoaxes, this study attempts to create an automatic English language hoax classification system using the Weighted Boosting ELM algorithm. The algorithm was chosen because it has high accuracy results for various types of document classification problems and has good results even if the data used has an unbalanced number of classes, making it suitable for hoax classifications which are fewer than factual news. The research methodology is divided into several stages consisting of pre-processing, term weighting, normalization, training and algorithm evaluation. The data used are 180 articles consisting of 90 hoax and 90 factual news. Evaluation was carried out by measuring F1 values ​​(results of average harmonic precision and recall) using K-Fold cross validation, the highest results obtained were 0,787.
Rekomendasi Lagu Cross Language Berdasarkan Lirik Menggunakan Word2VEC: ` Gilang Widianto Aldiansyah; Putra Pandu Adikara; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The song is a rhythmic variety of sounds and is one of the entertainment facilities that is popular among today's modern society. The desire of a society to listen to a song can be based on various things such as music (music genres), singers, years of songs to the lyrics of a song, singers, song years to song lyrics. In considering this matter, a system is needed that can facilitate the community in discussing a song based on the basics, one of which is a system of song contributions based on the lyrics. One method that can be used in building a song contribution system is Word Embedding which in this study uses Word2Vec. The stages of the system discussion of songs that contain data sets that produce a collection of words Word2Vec and TF-IDF, then the system will perform the processes of the lyrics of input songs by making expansion requests in accordance with the words words from TF-IDF close words the results of Word2Vec training. The process carried out by the system will produce 10 song titles that have similar lyrics to the input song. Results The aiming of the word parameters with TF-IDF taken and the close word test taken is the highest precision @ 10 value for the highest TF-IDF word taken = 25 and for the close word taken = 3.
Klasifikasi Genre Lagu dengan Fitur Akustik Menggunakan Metode K-Nearest Neighbor Husein Abdulbar; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Song cannot be separated from humans daily activities. When listening to songs humans can focus more on their activities. The rapid development of information on multimedia and electronic devices has led to a dramatic increase in music appreciation and creation. On the one hand this increase encourages people to enjoy songs more. But on the other hand, this increase forced the development of new technologies for the convenience of listening to songs. An example is how someone wants to find a song based on a song that has been heard. Genres classification is one of machine learning techniques that can group songs based on their usefulness. This technique can be used as a function in a system to support other functions, such as song recommendations, special word, or similar song searches. This study will use the K-Nearest Neighbor (K-NN) method as a genre classification technique for songs. To measure the similarity of two songs, a normalized cross correlation (NCC) equation is used to replace the distance calculation equation in the K-NN method. The features that extracted from a song are zero crossing rate, spectral centroid, spectral rolloff, and energy. Data obtained from feature extraction will be normalized using the z-score equation. The test results show that the best evaluation is obtained when the duration is 10, the offset is 120, and K in K-NN is 10. Precision, recall, and f-measure that obtained in this study are precision with a value of 0.637, recall with a value of 0.633, and f-measure with a value of 0.635.
Temu Kembali Citra pada Kue Tradisional berdasarkan Ekstraksi Fitur Color Histogram dan Color Moment menggunakan Algoritme Perhitungan Jarak Minkowski Andina Dyanti Putri; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Traditional cookies represent every single place in our country with its uniqueness. We can find the information on those culinary through various media such as books or the internet. It is straightforward if we know the name of the foods. However, the limitation of one's knowledge will bring different problems in acquiring the information, especially if it is text-based. The solution to that problem is to use Content-Based Image Retrieval (CBIR), a technique that automatically uses an image as a query and display a series of the similar image just like the query. We use feature extraction in CBIR, which is also a fundamental part of Image Analysis. The feature extraction is used Color Histogram and Color Moment. This research is using Minkowski distance calculation with following values: p = 1, p = 2, p = 3 and p = 5. The value of p = 3 in Minkowski distance calculation gives the best result to the combination of these two features is valued at 0,720498. The MAP average value which is acquired from top K-rank: k = 5, k = 10, k = 15, k = 20, and k = 25, is valued at 0,7119 for the features combination. We can conclude from this result that the feature extraction Color Histogram and Color Moment gives an excellent result for traditional cookies image analysis.
Co-Authors Adani, Rafi Malik Ade Kurniawan Adinda Chilliya Basuki Adinugroho, Sigit Adiyasa, Bhisma Adriansyah, Rachmat Afrizal Rivaldi Agi Putra Kharisma, Agi Putra Agus Wahyu Widodo Ahmad Fauzi Ahsani Akhmad Sa'rony Al Farisi, Faiz Aulia Al Huda, Fais Albert Bill Alroy Alimah Nur Laili Allysa Apsarini Shafhah Alqis Rausanfita Alvandi Fadhil Sabily Amaliah, Ichlasuning Diah Amar Ikhbat Nurulrachman Ananda Fitri Niasita Anang Hanafi Andina Dyanti Putri Andre Rino Prasetyo Anggraheni, Hanna Shafira Ani Budi Astuti Annisa Alifia Annisa, Zahra Asma Arsya Monica Pravina Aulia Jasmin Safira Aulia Rahma Hidayat Avisena Abdillah Alwi Azhar, Naziha Baliyamalkan, Mohammad Nafi' Barbara Sonya Hutagaol Bayu Andika Paripih Bayu Rahayudi Bryan Pratama Jocom Budi Darma Budi Darma Setiawan Candra Dewi Candra Dewi Dahnial Syauqy Daisy Kurniawaty Danang Aditya Wicaksana Dayinta Warih Wulandari Deri Hendra Binawan Dhanika Jeihan Aguinta Dheby Tata Artha Dian Eka Ratnawati Dika Perdana Sinaga Dimas Fachrurrozi Azam Dwi Suci Ariska Yanti Dwi Wahyu Puji Lestari Dyva Pandhu Adwandha Edy Santosa Eka Dewi Lukmana Sari Elmira Faustina Achmal Evilia Nur Harsanti Faiz Aulia Al Farisi Farid Rahmat Hartono Fattah, Rafi Indra Fayza Sakina Maghfira Darmawan Febriarta, Renaldy Dwisma Ferdi Alvianda Ferly Gunawan Ferly Gunawan Firdaus, Agung Firmansyah, Ilham Fitra Abdurrachman Bachtiar Franklid Gunawan Galih Nuring Bagaskoro George Alexander Suwito Gilang Widianto Aldiansyah Glenn Jonathan Satria Guedho Augnifico Mahardika Haekal, Firhan Imam Hanson Siagian Hendra Pratama Budianto Hernawan, Yurdha Fadhila Hibatullah, Farras Husain Husein Abdulbar Ichsan Achmad Fauzi Ika Oktaviandita Imam Cholisoddin Imam Cholissodin Imam Ghozali Imanuel Juventius Todo Gurning Indah Mutia Ayudita Indriati Indriati Indriati Indriya Dewi Onantya Ivan Fadilla Ivan Ivan Jesika Silviana Situmorang Jojor Jennifer BR Sianipar Jonathan Reynaldo Junda Alfiah Zulqornain Karina Widyawati Karunia Ayuningsih Katherine Ivana Ruslim Khalisma Frinta Krishnanti Dewi Laila Restu Setiya Wati Lailil Muflikhah Laksono Trisnantoro Lubis, Saiful Wardi Lusiyana Adetia Isadi Luthfi Mahendra M. Aasya Aldin Islamy M. Ali Fauzi Maghfiroh, Sofita Hidayatul Makrina Christy Ariestyani Marina Debora Rindengan Maya Novita Putri Riyanto Mayang Arinda Yudantiar Mayang Panca Rini Melati Ayuning Lestari Moch. Khabibul Karim Moh. Dafa Wardana Mohammad Fahmi Ilmi Mohammad Toriq Muh. Arif Rahman Muhammad Faiz Al-Hadiid Muhammad Fajriansyah Muhammad Iqbal Pratama Muhammad Nurhuda Rusardi Muhammad Rizaldi Muhammad Rizky Setiawan Muhammad Tanzil Furqon Muhammad Taufan Muthia Azzahra Nadhif Sanggara Fathullah Nadia Siburian Nanda Agung Putra Nanda Cahyo Wirawan Naufal Akbar Eginda Naziha Azhar Niluh Putu Vania Dyah Saraswati Novan Dimas Pratama Novanto Yudistira Nur Hijriani Ayuning Sari Nurul Hidayat Panjaitan, Mutiharis Dauber Panji Husni Padhila Pengkuh Aditya Prana Prais Sarah Kayaningtias Prakoso, Andriko Fajar Pretty Natalia Hutapea Putri Rahma Iriani Radita Noer Pratiwi Rahma Chairunnisa Raissa Arniantya Randy Cahya Wihandika Randy Cahya Wihandika Randy Ramadhan Ravindra Rahman, Azka Renata Rizki Rafi` Athallah Renaza Afidianti Nandini Restu Amara Rezky Dermawan Rhevitta Widyaning Palupi Ridho Agung Gumelar Riza Cahyani Rizal Maulana, Rizal Rizal Setya Perdana Rizal Setya Perdana Rosy Indah Permatasari Sagala, Revaldo Gemino Kantana Salsabila Insani Salsabila Rahma Yustihan San Sayidul Akdam Augusta Santoso, Nurudin Sigit Adinugroho Sigit Adinugroho Silaban, Gilbert Samuel Nicholas Silvia Ikmalia Fernanda Sindy Erika Br Ginting Sri Indrayani, Sri Sutrisno Sutrisno Tania Malik Iryana Taufan Nugraha Thariq Muhammad Firdausy Tibyani Tibyani Tirana Noor Fatyanosa, Tirana Noor Uke Rahma Hidayah Utaminingrum, Fitri Vergy Ayu Kusumadewi Vinesia Yolanda Vivin Vidia Nurdiansyah Wijanarko, Rizqi Yerry Anggoro Yohana Yunita Putri Yoseansi Mantharora Siahaan Yosua Dwi Amerta Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari Yulia Kurniawati Yurdha Fadhila Hernawan Yure Firdaus Arifin Zahra Asma Annisa