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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Imam Cholissodin
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Optimasi Rute Distribusi Produk PT Indomarco Adi Prima (Stock Point Nganjuk) Dengan Algoritma K-Means Dan Ant Colony Optimization (K-ACO) Wahyu Bimantara; Bayu Rahayudi; Imam Cholissodin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Product distribution companies require cost-effectiveness and efficiency, one of the supporting factors a determining the optimal distribution route. The distribution route is closely related to the Traveling Salesman Problem. In the distribution process from the warehouse or stock point, Nganjuk PT Indomarco Adi Prima has a Multiple Traveling Salesman Problem which involves more than one salesman in the distribution process. To solve MTSP problems, you can search for it by traveling to all possible routes. However, when there are more routes, more time is needed. This research is an effort to optimize the distribution route of PT Indomarco Adi Prima's Nganjuk stock point products using the K-Means and Ant Colony Optimization (K-ACO) methods, in which the K-Means method a used to divide MTSP problems into smaller problems than each problem. Then each of these problems will find the shortest route with ACO. In the tests carried out, K-ACO can save salesmen a traveling distance of 565.801 km. While testing using the Silhouette Coefficient, K-Means resulted in a 76.72% better solution when compared to the results of real sales trips. These results indicate that the use of K-ACO can minimize the total distance traveled from the problem.
Sistem Pakar Diagnosis Penyakit Gagal Jantung Kongestif, Penyakit Paru Obstruktif Kronik, Dan Asma Berdasarkan Gejala Utama Sesak Kronik Menggunakan Kombinasi Metode K-Nearest Neighbor Dan Certainty Factor Jeffrey Junior Tedjasulaksana; Imam Cholissodin; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Health is very important for everyone's life and if it is not cured immediately, it can interfere with activities so it can cause death. According to several studies, one of the diseases that is often experienced by everyone is a disease with symptoms of shortness of breath or difficulty breathing. Chronic shortness is most often caused by heart diseases such as congestive heart failure or respiratory disease, asthma and chronic obstructive pulmonary disease (COPD). Several studies reported that the compatibility between a diagnosis by a general practitioner in primary health care and a final diagnosis by a specialist is only less than 50%. So in this study an expert system was made to diagnose congestive heart disease (CHF), chronic obstructive pulmonary disease (COPD), and asthma using a combination of the K-Nearest Neighbor method to classify diseases with the Certainty Factor method to determine the level of confidence from the previous classification results using 20 symptoms. The data used is patient data at Jumpandang Baru Public Health Center in Makassar City with a total of 100 data. The best accuracy results in testing variations in the K value are 100% when the K value is 3 and the results of testing the comparison of accuracy when using a combination of the K-Nearest Neighbor - Certainty Factor method and when only using the K-Nearest Neighbor method it produces the same accuracy value.
Rekomendasi Pengambilan Judul Skripsi Menggunakan PSO-Neighbor Weighted K-Nearest Neighbor (NWKNN) (Studi Kasus: Jurusan Ilmu Keolahragaan Fakultas Ilmu Keolahragaan Universitas Negeri Medan) Rizky Ramadhan; Imam Cholissodin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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This research is non-implementative descriptive with the K-Means technique as the initial formation of the clusters of graduated student thesis titles, the PSO technique as a course selection, and the Neighbor Weighted K-Nearest Neighbor (NWKNN) technique as data classification and algorithm performance measurement using the technique. accuracy. The data collection of this research is in the form of document studies from 2016/2019 graduate students from the Faculty of Sport Sciences, State University of Medan. The purpose of this study was to determine the parameter value and accuracy value of the application of the NWKNN algorithm to provide the best recommendations regarding the thesis title raised by students. The results of this study can be concluded that in testing the percentage of many comparisons of training data and testing data used is 90%: 10%. The generation and feature testing resulted in a generation that began to be constant in the 50th generation and with 15 subjects, namely: MK2, MK6, MK7, MK11, MK12, MK13, MK14, MK15, MK21, MK24, MK26, MK27, MK28, MK31, MK32. In testing the K value, the optimum K value is 3. In testing the K and E values, the optimum K and E values ​​are 3 and 2. The PSO-Neighbor Weighted K-Nearest Neighbor (NWKNN) algorithm for recommendations for thesis title retrieval produces an optimum value using parameters previously obtained an accuracy of 88.28%.
Implementasi Integrasi K-Means dan Naive Bayes dalam Identifikasi Tingkat Risiko Reksa Dana Kukuh Wicaksono Wahyuditomo; Imam Cholissodin; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mutual funds are investment instruments that collect investors' funds, to be invested in securities within the mutual fund itself, with general parameters such as Net Asset Value (NAV) and time-bound returns. Both of these parameters have varying values, so they can act as a risk measure that can affect the profit of mutual funds. The effect of this risk makes people hesitate to invest in mutual funds because the level is not known based on those two parameters so that identification is involved to help determine the level of risk for mutual funds, which in this study used the integration of K-Means and Naive Bayes. The K-Means algorithm as a clustering algorithm is used to group mutual funds which then the results of the group into data classes to be classified by the Naive Bayes algorithm. The study used 250 mutual funds data on September 1, 2020, from the types of stock, money market, and mixed mutual funds. This study tested the number of clusters and the percentage amount of training data and test data. The test results showed that the optimal number of clusters was 4 with a global Silhouette Coefficient of 0,46448 and average of all classes from the evaluation of the classification model based on the best data amount percentage involving 4 classes in the form of precision of 0,9813, recall of 0,9818, and F-measure of 0,9808.
Optimasi Penjadwalan Pekerja Shift di Rumah Makan Cepat Saji (Fast Food Restaurant) menggunakan Algoritma Genetika (Studi Kasus: Warung Gunung di Kediri) Ellita Nuryandhani Ananti; Imam Cholissodin; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Fast food restaurants are one of the businesses in the culinary field that are developing very rapidly at this time. Labor plays an important role in providing services to buyers where it can also affect income and also support the economy of a restaurant business, one of which is Warung Gunung (Wagu) in Kediri. In Wagu itself, there is a different division of job desc for each group of workers. When scheduling manually, it also takes a long time and is prone to human error. Therefore, it is necessary to have computerized scheduling of workers to carry out production activities and services to consumers effectively and efficiently. In this study, the scheduling process is carried out using the Genetic Algorithm method starting from the representation of chromosomes to the worker code and division, then carrying out a one-cut point crossover and reciprocal exchange mutation process to get new offspring which are then used in the selection process using elitism selection for the next generation. Based on the results of the parameter testing that has been carried out, it produces the most optimal solution without any violation of shift worker scheduling by producing the largest average fitness value is 1 which is found in the population size of 520, the number of generations is 450, and the combination of cr and mr is 0,6:0.4.
Maximum Power Point Tracking (MPPT) pada Panel Surya dalam Kondisi Berbayang Sebagian dengan Particle Swarm Optimization (PSO) Muhammad Rois Al Haqq; Imam Cholissodin; Arief Andy Soebroto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 8 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Photovoltaic (PV) array under partial shade condition due to clouds or the shadow of buildings will make PV array get uneven irradiation. PV array with uneven irradiation will form multiple peaks on characteristic curve and will make the characteristic curve complex. In under partial shade condition, the conventional maximum power point tracking (MPPT) method can't reach to global MPP. MPPT based on meta-heuristic method can solve the PV characteristic curve problem that have multiple peaks. One of the meta-heuristic methods that can be used is Particle Swarm Optimization (PSO). The PSO method is used to solve the problem of the global maximum power under partial shade condition. The PV system consist of PV array, boost converter and MPPT implemented in MATLAB/Simulink. Experiments were carried out on 3 possible shadow patterns on 5 series connected moduls. The experimental results shows that overall MPPT PSO obtained an average tracking efficiency of 99,4275% with an average tracking time of 1.04 seconds. MPPT PSO can track the maximum power under partial shade condition.
Sentimen Analisis Layanan Produk Indihome menggunakan Information Gain dan Metode K-Nearest Neighbor Atika Anggraeni; Imam Cholissodin; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 8 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The development of the times from time to time increasingly influences the digital era in various parts of the world, because the development of this era requires an internet network. Users who need an internet network come from various groups, from students to workers. The increasing of internet network labor every year so that it can generate profits for internet service providers, one of which is PT. Telekomunikasi Indonesia, Tbk (Telkom). Users on the internet network provided by this company are people throughout Indonesia. Because of this, it is possible that there are encouraging suggestions or complaints from customers. In this study using responses from the public in the form of positive comments and negative comments. To find out whether these comments are positive or negative, you must carry out several stages of analysis to get the final result. The steps taken are pre-processing, Information Gain feature selection, term weighting and classification of the K-Nearest Neighbor (KNN) algorithm. This study uses 480 training data and 120 test data. This study obtained the highest accuracy value of 86.67%, with a precision of 94.44%, a recall of 81.73%, and f-measure of 91,30%.
Peringkasan Teks menggunakan metode Maximum Marginal Relevance terhadap Artikel Berita terkait COVID-19 Yudha Ananda Kresna; Imam Cholissodin; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

At this time, people can easily search and get information or news, both news through television and news from online media. The number of facilities that support the public to read the news causes the number of newsreaders in Indonesia increase too. However, many news articles found that the number of words and the use of words are less effective so it would be a waste of time when reading the entire contents of the news. From these problems, it takes a system that is able to summarize the content of the news in order for the news content to become dense. To summarize the content of the news, in this study used a method that is Maximum Marginal Relevance to produce a summary. In the method required several stages including, preprocessing, weighting TF-IDF, weighting cosine similarity and maximum marginal relevance method itself. This study was conducted by taking 30 samples of news article data with the theme COVID-19 from the website of online news provider kompas.com. Obtained the following test results, the best value regulator coefficient is α=0.5 with precission result = 0.684333, recall = 0.772 and f-measure = 0.7. While based on the number of words, the number of words translated to 300 produces the best f-measure value with a value of 0.726923. As well as being tested systems with and without stemming and the result the system using stemming produces a better summary than the system without stemming.
Penerapan Metode Extreme Learning Machine (ELM) dengan Optimasi Particle Swarm Optimization (PSO) untuk memprediksi Harga Cabai Keriting di Kota Malang Tara Dewanti Sukma; Imam Cholissodin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Curly chili is a basic necessity for the people of Malang City, namely as a complement to cooking spices so that its existence is often sought after. This causes fluctuation due to the influence of the amount of demand on price change. So a price prediction system for curly chilies is needed in Malang City to minimize price instability. Extreme Learning Machine (ELM) is a prediction method that has high accuracy and faster execution time. ELM does not have a feature selection function, so an optimization method such as Particle Swarm Optimization (PSO) is needed. PSO is implemented as a solution to get optimal weight with the fitness value as a comparison. Based on the tests that have been carried out on the price of curly chilies, the average MAPE value is 1,133803% and the average fitness value is 0,400346 with optimal parameters consisting of 2 features, hidden neuron is 3, the percentage comparison between training and testing data is 90%: 10%, the weight of inertia is 0,5, c1 is 3, c2 is 1,5, the lower speed limit value is -0,8, the speed upper limit value is 0,8, the population is 100, and it is carried out by 260 iterations. From the test results, it can be concluded that PSO is able to optimize the ELM weight so it could gets optimal accuracy.
Klasifikasi Dokumen E-Complaint Kampus menggunakan BM25 dan K-Nearest Neighbor Khairinnisa Rifna; Imam Cholissodin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 10 (2021): Oktober 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In an educational institution, a forum is absolutely needed where the academic community severely provides suggestions and criticisms about what they feel about the educational institution facilities such as a forum for criticism and suggestions provided by Brawijaya University, namely e-complaint. E-complaint is a facility managed by the campus as a means of accommodating suggestions, criticisms, opinions from users regarding services or facilities provided by the campus. However, the e-complaint facility is currently considered to be less than optimal because the criticism or suggestions submitted by the academic community are not processed quickly by the parties concerned. This is because the e-complaint manager sorts the documents manually which causes a long time process and the e-complaint manager does not sort the documents based on importance and urgency so that the process of solving the e-complaint's problem is not sorted by the priority of the urgency. Therefore, a system is needed that can classify campus e-complaint documents based on their level of importance and urgency. In this study, a text pre-processing process was carried out, which was then carried out using the BM25 method as a ranking method and the K-Nearest Neighbor method as a classification method. Based on the test results using k-fold cross validation, the highest average value of precision is 1, recall value is 0,46875, f-measure value is 0,6875.
Co-Authors Achmad Arwan Adam Syarif Hidayatullah Adhipramana Raihan Yuthadi Adhitya Wira Castrena Adinugroho, Sigit Ageng Wibowo Agus Wahyu Widodo Aldino Caturrahmanto Alfen Hasiholan Alif Fachrony Ana Holifatun Nisa Anandita Azharunisa Sasmito Andika Eka Putra Andriko Hedi Prasetyo Anggi Novita Sari Anim Rofi'ah Annisa Alifia Annisaa Amalia Safitri Aqmal Maulana Tisno Nuryawan Ardiansyah Setiajati Arief Andy Soebroto Arina Indana Fahma Arsti Syadzwina Fauziah Atika Anggraeni Aulia Dinia Aulia Herdhyanti Aulia Jasmin Safira Azmi Makarima Yattaqillah Bahruddin El Hayat Bana Falakhi Bayu Andika Paripih Bayu Rahayudi Benita Salsabila Bisma Anassuka Bondan Sapta Prakoso Brendy Oscar Munthe Brigitta Ayu Kusuma Wardhany Budi Darma Setiawan Budi Santoso Candra Dewi Cindy Cynthia Nurkholis Citra Nadya Dwi Irianti Daisy Kurniawaty Danastri Ramya Mehaninda Daneswara Jauhari Daniel Agara Siregar Dellia Airyn Diah Priharsari Dian Eka Ratnawati Dieni Anindyasarathi Dinda Adilfi Wirahmi Diva Kurnianingtyas Dyah Ayu Wahyuning Dewi Edy Santoso Ega Ajie Kurnianto Elisa Julie Irianti Siahaan Ellita Nuryandhani Ananti Elmira Faustina Achmal Ema Agasta Ema Rosalina Eriq Muh. Adams Jonemaro Ersya Nadia Candra Fahri Ariseno Faizatul Amalia Faturrahman Muhammad Suryana Fayza Sakina Maghfira Darmawan Febriyani Riyanda Felicia Marvela Evanita Fendra Gunawan Ficry Agam Fathurrachman Fikhi Nugroho Fildzah Amalia Firda Priatmayanti Fitra Abdurrachman Bachtiar Franklid Gunawan Galih Ariwanda George Alexander Suwito Ghulam Mahmudi Al Azis Gregorius Dhanasatya Pudyakinarya Guruh Adi Purnomo Gusti Reza Maulana Heny Dwi Jayanti Heru Nurwarsito Himawat Aryadita Holiyanda Husada Husin Muhamad I Gusti Ayu Putri Diani Ibnu Rasyid Wijayanto Ichwanda Hamdhani Ika Oktaviandita Indriati Indriati Irma Lailatul Khoiriyah Ishak Panangian Sinaga Istiana Rachmi Izzatul Azizah Jeffrey Junior Tedjasulaksana Khairinnisa Rifna Khairiyyah Nur Aisyah Komang Anggada Sugiarta Kresentia Verena Septiana Toy Kukuh Wicaksono Wahyuditomo Laila Restu Setiya Wati Lailil Muflikhah Leni Istikomah Liwenki Jus'ma Olivia M. Ali Fauzi M. Khusnul Azhari Mahendro Agni Giri Pawoko Marji Marji Maulana Ahmad Maliki Maulana Putra Pambudi Mauldy Putra Pratama Mentari Adiza Putri Nasution Michael David Moch Bima Prakoso Moh. Ibnu Assayyis Mohammad Aditya Noviansyah Mohammad Angga Prasetya Askin Mohammad Toriq Muhammad Aghni Nur Lazuardy Muhammad Dio Reyhans Muhammad Fahmi Hidayatullah Muhammad Fuad Efendi Muhammad Halim Natsir Muhammad Hasbi Wa Kafa Muhammad Hidayat Muhammad Maulana Solihin Hidayatullah Muhammad Nadzir Muhammad Rizal Ma'rufi Muhammad Rois Al Haqq Muhammad Shafaat Muhammad Syafiq Muhammad Tanzil Furqon Muhammad Taufan Mukh. Mart Hans Luber Nabila Lubna Irbakanisa Nabilla Putri Sakinah Nadia Natasa Tresia Sitorus Nadia Siburian Nadiah Nur Fadillah Ramadhani Nining Nahdiah Satriani Noerhayati Djumaah Manis Novanto Yudistira Novirra Dwi Asri Nur Afifah Sugianto Nur Firra Hasjidla Nurul Hidayat Nurul Inayah Obed Manuel Silalahi Panji Husni Padhila Priscillia Vinda Gunawan Putra Pandu Adikara Putri Ratna Sari Radita Noer Pratiwi Randy Cahya Wihandika Ratih Kartika Dewi Rayhan Tsani Putra Renata Rizki Rafi` Athallah Restu Fitriawanti Reyvaldo Aditya Pradana Reza Aprilliana Fauzi Rien Difitria Rinindya Nurtiara Puteri Rio Cahyo Anggono Riski Ida Agustiyan Rizal Aditya Nugroho Rizal Setya Perdana Rizaldy Aditya Nugraha Rizky Ramadhan Rosintan Fatwa Rowan Rowan Sabrina Nurfadilla Salsabila Multazam Sandya Ratna Maruti Sari Narulita Hantari Satria Habiburrahman Fathul Hakim Sayyidah Karimah Shafira Eka Aulia Putri Shelly Puspa Ardina Shibron Arby Azizy Shinta Anggun Larasati Siti Mutdilah Sofi Hidyah Anggraini Stefanus Bayu Waskito Supraptoa Supraptoa Sutrisno Sutrisno Tara Dewanti Sukma Tibyani Tibyani Timothy Bastian Sianturi Tobing Setyawan Tony Faqih Prayogi Tusiarti Handayani Tusty Nadia Maghfira Uke Rahma Hidayah Uswatun Hasanah Utaminingrum, Fitri Vergy Ayu Kusumadewi Veronica Kristina Br Simamora Vinesia Yolanda Vivilia Putri Agustin Vivin Vidia Nurdiansyah Wahyu Bimantara Wanda Athira Luqyana Wicky Prabowo Juliastoro Windy Adira Istiqhfarani Yessica Inggir Febiola Yoseansi Mantharora Siahaan Yudha Ananda Kresna Yudo Juni Hardiko Yuita Arum Sari Yunico Ardian Pradana Yusuf Afandi Zanna Annisa Nur Azizah Fareza