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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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Segmentasi Pelanggan menggunakan Metode Kernel K-Means (Studi Kasus: Smartlegal.id) Elmira Faustina Achmal; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 6 (2022): Juni 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Smartlegal.id is a company engaged in the field of law. As of 2021, SmartLegal.id has taken care of more than 60,492 business legalities throughout Indonesia since 2014. According to Smartlegal.id's Product Specialist Manager, analysis of client behavior with the aim of forming marketing strategies is still experiencing time constraints, because it requires scheduling face-to-face meetings with clients. In addition, the marketing problem that often occurs is the alignment between content, events, promos and also market needs which can take a long time to match and align. Based on these problems, a practical analysis of client behavior is needed to save time. One technique that can be used is clustering. The K-Means method is a method that has been widely implemented by Information Technology practitioners. However, with the high dimensions of the existing data, it is necessary to adjust the method by adding a kernel function so that it can better classify non-linearly separable data. From the results of the research conducted, the best Silhouette Score was 0,9035 using 2 clusters, the Polynomial kernel function with the Polynomial degree parameter was 30, and the data percentage was 100%. This study also conducted a comparison of the effectiveness between K-Means and Kernel K-Means in segmenting.
Analisis Sentimen terhadap Aplikasi PeduliLindungi menggunakan Metode Long Short-Term Memory (LSTM) Muhammad Rizaldi; Putra Pandu Adikara; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 13 (2022): Publikasi Khusus Tahun 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Dipublikasikan di Jurnal Teknologi dan Sistem Komputer
Prediksi Harga Bitcoin berdasarkan Data Historis Harian dan Google Trend Index menggunakan Algoritme Extreme Learning Machine Panji Husni Padhila; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 7 (2022): Juli 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Bitcoin has attracted a lot of attention from the media and investors, given its innovative features such as its decentralization and traceability. In some countries have accepted Bitcoin as a means of payment. Bitcoin is also commonly used as an investment asset although it is quite dangerous because the price of Bitcoin is very volatile, which means that the price can go up and down quickly in a short time. In addition, Bitcoin is also believed to be speculative, whose price goes up and down depending on people's views of the coin. This study aims to predict the price of Bitcoin using the Extreme Learning Machine (ELM) algorithm based on daily historical data by considering its speculative nature using the Google Trend Index. Based on the results of the tests carried out, the results of the Mean Absolute Percentage Error (MAPE) calculation are 3,089% using the Sigmoid activation function, 5 features, 20 neurons in the hidden layer, and using Google Trend Index with the keyword 'Bitcoin'.
Prediksi Potensi Pengidap Penyakit Diabetes berdasarkan Faktor Risiko Menggunakan Algoritme Kernel K-Nearest Neighbor Renata Rizki Rafi` Athallah; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 8 (2022): Agustus 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Diabetes is a chronic disease characterized by high blood sugar. As of 2011, there were 7.29 million people suffering from diabetes, and in 2021, there were 19.47 million people who have diabetes. The percentage increase in people with diabetes from 2011-2021 has a percentage increase of 267%. Very rapid growth and one of the causes of death worldwide is a problem that needs to be solved. Reduce the number of people with diabetes, there are various ways, but they are not optimal. So it is necessary to research to develop a system that can detect diabetes early so that treatment or prevention can run well. One of the techniques that can be used to detect diabetes early is prediction. The K-Nearest Neighbor (K-NN) algorithm is an algorithm designed to classify data based on previously classified learning data however this algorithm has a weakness in processing data that has high dimensions and is non-linearly separable, so adding a kernel function is a good choice for input data clustering. From the results of this study, the value of k and the kernel function with the highest accuracy value is k = 50. The kernel function Linear and Polynomial degree 1 and the performance of the Kernel K-Nearest Neighbor algorithm are better than the K-Nearest Neighbor algorithm with a difference in the accuration value of 0.14.
Analisis Sentimen IMDB Movie Reviews menggunakan Metode Long Short-Term Memory dan FastText M. Aasya Aldin Islamy; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Current technological developments make it easier for humans to explore a lot of information using the internet such as review information or opinions about films. Public opinion about the film can be found on the IMDB website. By doing a sentiment analysis on public opinion about the film, we can conclude whether a film gets more positive or negative opinions. To perform this sentiment analysis, one of the deep learning methods is used, namely Long Short-Term Memory (LSTM) with FastText as a vector representation of words in the IMDB movie reviews dataset of 50,000 data. Performance using the Long Short-Term Memory and FastText methods produces an accuracy of 0.863; precision of 0.865; recalls of 0.861; and f1-score of 0.863. This LSTM and FastText method produces better performance than using LSTM alone with a difference of 0.053 on the f1-score value with details of accuracy reaching 0.808; precision reaches 0.804; recalls reached 0.816; f1-score reaches 0.810 for the LSTM method only.
Pencarian Dokumen Skripsi menggunakan BM25 dan Faceted Search berdasarkan Kata Kunci Abstrak (Studi Kasus: Universitas Muhammadiyah Sidoarjo) Mohammad Fahmi Ilmi; Putra Pandu Adikara; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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A thesis is a graduation requirement for students to complete their studies. However, along with the much research that students have carried out, many universities are not ready to provide a forum that can make it easier for students to access the required thesis files, one of which is the Muhammadiyah University of Sidoarjo. To overcome this problem, the researchers developed a program that focuses on finding the thesis document with the help of Information Retrieval. Information Retrieval can help sort out which thesis data is appropriate and will group it into data groups given to seekers. As an improvement, this program is also given an additional feature in the form of category grouping, which is done using Faceted Search based on the keywords of each document. In this research, the BM25 method is used to prove whether this method can produce good accuracy in searching for student thesis data. The tests carried out on 25 queries resulted in the most considerable average value at the value of k=5, with a value of 0.928. This shows that the search results for the most relevant documents are collected in the top 5 documents.
Prediksi Penerimaan Mahasiswa Baru dengan Menggunakan Metode Extreme Learning Machine (ELM) (Studi Kasus pada Universitas 17 Agustus 1945 Surabaya) Aulia Jasmin Safira; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Admission of new students is a routine activity carried out by all educational institutions in Indonesia every year, which is a reflection of the public's views and interests in the educational institution. Predictions of the development of new student admissions so far have only been made based on speculation using data from previous years. An Extreme Learning Machine (ELM) is one of the methods that can be used to predict good results. Therefore, this study used the Extreme Learning Machine (ELM) method. The results of the trial in this study showed that the ELM method has a good error value measured by an error rate using the Mean Absolute Percentage Error (MAPE) of 0,20% with a comparison of the amount of training data and testing data of 90%:10%, the input weight range between-0.5 and 0.5, the number of neurons in the hidden layer as many as 2, using the Binary Sigmoid activation function, and using the number of features 2. This proves that using the Extreme Learning Machine (ELM) method, it can predict new student admissions well and get the number of new student admissions in the future.
Analisis Sentimen Citayam Fashion Week pada Komentar YouTube dengan Metode Convolutional Neural Network George Alexander Suwito; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Citayam Fashion Week's virality started with the circulation of interview videos on Tiktok and Instagram. The positive impact was felt especially for traders due to the high number of visitors who wanted to see the event so that they could increase their turnover, apart from the positive impact felt, traffic jams arise due to the high number of visitors and the location of the fashion show action, thereby disrupting the activities of the general public. Citayam Fashion Week was disbanded on Wednesday, 27 July 2022. This caused debate among the public. Some people support the holding of the event, some people do not support holding the event. Various positive, and negative comments were given by the public to the Citayam Fashion Week event, therefore a method is needed that can automatically sort out user sentiments. and efficiently, avoiding public misperceptions of existing comments. In this study the authors used the Convolutional Neural Network (CNN) method in conducting sentiment analysis, based on the results of the tests that have been carried out, this system has the best metric evaluation value, namely an accuracy value of 97%, a precision value of 97%, a recall value of 98%, and the f-measure value of 97%.
Ekstraksi Ciri pada Klasifikasi Citra Batik menggunakan Metode Gray Level Co-Occurrence Matrix, Local Binary Pattern, dan HSV Color Moment Amar Ikhbat Nurulrachman; Randy Cahya Wihandika; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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One form of art passed down by the ancestors of the Indonesian nation is batik, batik in every region in Indonesia has a variety of colors and motifs. The diversity of colors and motifs of batik makes it difficult for many Indonesians to know the type of batik they are wearing. Every batik has a pattern, every pattern has a texture. Texture and color are the distinguishing elements between one batik and another, both are forms of feature extraction that can be used to group batiks that have similar patterns. In this study, a combination of Gray Level Co-Occurrence Matrix, Local Binary Pattern, and HSV Color Moment features was used to obtain texture and color characteristics from batik images, while K-Nearest Neighbor was used to classify batik images. Test results on scenarios using different feature combinations, a combination of features Gray Level Co-Occurrence Matrix, Local Binary Pattern, and HSV Color Moment using 200 batik image datasets consisting of 10 batik classes, obtain the highest accuracy value of 0.29 on the neighbor value K=5, on the other hand, in the test scenario using a different number of classes, the highest accuracy value is obtained when using 5 classes, each class consisting of 10 batik images, the accuracy value is 0.68 at the neighbor value K = 4.
Analisis Sentimen terhadap Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) Level 3 berdasarkan Data Twitter menggunakan Algoritma Naive Bayes Annisa Alifia; Imam Cholissodin; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The rapid development of technology has made it easier for people to express their aspirations. These aspirations can be channeled through social media which is currently increasingly popular among the public. One of the social media that is often used by Indonesian citizens is Twitter. In February 2022, Community Activities Restrictions Enforcement Level 3 had become a trending topic on Twitter, which indicated that the increase in the level elicited various responses from the public. Community Activities Restrictions Enforcement (CARE) is one of the policies that the government has implemented in tackling Covid cases in Indonesia. Public opinion regarding this issue will generate various sentiments that can be analyzed. In this study, sentiment analysis will be carried out on public opinion regarding the increase in Community Activities Restrictions Enforcement to level 3 using the Multinomial Naive Bayes algorithm. The process consists of data pre-processing, word weighting with Raw Term Frequency, training and testing of the Naive Bayes model which later the accuracy results will be calculated using the K-Fold Cross Validation of 5 folds. This study produces an average accuracy of 0.78 with the addition of stop words and data normalization. This accuracy does not create much difference without using stopwords and data normalization with an accuracy of 0.79. The addition of stopwords and data normalization still does not produce a significant difference.
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