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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TEKNOLOGIA Jurnal Linguistik Komputasional Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Journal of Data Science and Its Applications Charity : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer Prosiding Konferensi Nasional PKM-CSR Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Indonesia Sosial Teknologi eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Dike Jurnal INFOTEL CSRID
Kemas Muslim Lhaksmana
Universitas Telkom, Indonesia
Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Prospek dan Keefektifan Penggunaan Google Classroom untuk Pembelajaran: Evidence dari SMP Pajajaran 1 Bandung deni saepudin; didit adytia; kemas muslim
Charity : Jurnal Pengabdian Masyarakat Vol 4 No 2 (2021): Charity-Jurnal Pengabdian Masyarakat
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/charity.v4i2.3436

Abstract

Akibat pandemi Covid-19, masyarakat diminta untuk menjaga jarak fisik (physical distancing) untuk mencegah penularan. Salah satu konsekwensi dari keharusan menjaga jarak fisik ini adalah proses pembelajaran tidak dilakukan di sekolah, namun dilakukan dari rumah melalui media daring. Namun, belum semua sekolah siap melaksanakan pembelajaran daring. Salah satu faktor ketidaksiapan pembelajaran daring adalah pengetahuan/keterampilan guru dalam memanfaatkan media pembelajaran daring. Ada beberapa media online yang dapat digunakan untuk proses pembelajaran daring, salah satunya adalah Google Classroom. Dengan keterampilan para guru dalam memanfaatkan Google Classroom, proses pengajaran daring masih dapat berlangsung meskipun dengan banyak keterbatasan, baik keterbatasan perangkat maupun keterbatasan jaringan. Sampai saat ini, pandemi Covid-19 masih berlangsung dan belum diketahui kapan berakhir. Dengan keterampilan memanfaatkan Google Classroom, SMP Pajajaran 1 Bandung masih dapat melaksanakan pembelajaran daring. Pembelajaran ini juga dapat menjadi pelengkap proses pembelajaran konvensional di saat normal setelah pandemi berakhir dan ada potensi menggeser hasil studi sebelumnya yang menyebutkan pembelajaran dengan platform Google Form tidak terlalu efektif
VILLAGE RESOURCE MANAGEMENT SYSTEM UNTUK MENDUKUNG TATA KELOLA DESA SUNDAWENANG, SUKABUMI, JAWA BARAT Kemas Muslim Laksamana; Eko Darwiyanto; Dana Sulitstyo Kusumo
Charity : Jurnal Pengabdian Masyarakat Vol 1 No 1 (2018): Charity - Jurnal Pengabdian Masyarakat
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/charity.v1i01.1586

Abstract

Desa Sundawenang di Kabupaten Sukabumi, Jawa Barat, merupakan desa yang memiliki banyak potensi, antara lain posisi yang strategis di jalan raya Sukabumi-Bogor, produksi singkong, dan terdapat sejumlah industri seperti pabrik otomotif, elektronik, boneka, wig, dan sebagainya. Kondisi desa tersebut mengundang sejumlah besar pendatang, sehingga diperkirakan 40% penduduk desa bukanlah penduduk asli setempat. Tersebarnya sejumlah industri di wilayah Desa Sundawenang juga berpengaruh terhadap mata pencaharian warga. Tercatat sebanyak 41% warga berprofesi sebagai karyawan, kemudian disusul dengan wiraswasta dan jasa (28%), petani (27%), dan PNS (4%). Dengan karakteristik tersebut, Kepala Desa Sundawenang yang baru menjabat pertama kali memerlukan data dan berbagai laporan untuk menyusun berbagai program dan kebijakan desa yang sesuai. Village resource management system (VRMS) dibangun untuk memberikan solusi bagi kebutuhan Kepala Desa tersebut. VRMS terdiri dari tiga aplikasi utama, yaitu aplikasi pelayanan desa, aplikasi pengelolaan data desa, dan aplikasi analisis data. Aplikasi pelayanan desa telah diresmikan dan diberikan pelatihan penggunaannya kepada aparat Desa Sundawenang. Diharapkan aplikasi ini dapat diterapkan pada desa-desa lain di Indonesia dengan penyesuaian yang dapat dilakukan untuk mengakomodasi kebutuhan yg lebih khusus.
Fully Communication Oriented Information Modeling On SME Information Systems Development Ela Nadila; Kemas Muslim Lhaksmana; Seno Adi Putra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3327

Abstract

Information modeling a very important role in the development of information systems today. Information modeling is an activity to create a conceptual model that includes all significant information in business processes. Using information modeling, a machine is needed for implementation, and redundancy and anomaly problems are also common in databases. This problem arises when the database is not normalized. To solve the problem, this research will analyze the SME information system using the Fully Communication Oriented Information Modeling  (FCO-IM) method and compare it with the method without FCO-IM made by 12 designers. The results of the analysis, information modeling using FCO-IM method can produce a relational data schema that already meets 3NF normalization and is suitable for implementation in SME information system development so that it can be used as an option in data modeling and can avoid data redundancy problems.
Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification Sindi Fatika Sari; Kemas Muslim Lhaksmana
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2099

Abstract

Employee attrition is the loss of employees in a company caused by several factors, namely employees resigning, retiring, or other factors. Employee attrition of employees can have a negative impact on a company if it is not handled properly, including decreased productivity. The company also requires more time and effort to recruit and train new employees to fill vacant positions. This attrition prediction aims to help the human resources (HR) department in the company to find out what factors influence the occurrence of employee attrition. This research implements Random Forest while comparing Information Gain, Select K Best, and Recursive Feature Elimination feature selection methods to find which feature selection produces the best performance. The implementation of the aforementioned methods outperforms previous research in terms of accuracy, precision, recall, and f1 scores. In preparing this research, the first author collects data sets, makes programs, and compiles journals. The second author assists the first author in programming and preparing the journal. From the results of the tests that have been carried out, Information Gain produces the highest accuracy value of 89.2%, while Select K Best produces an accuracy value of 87.8% and Recursive Feature Elimination produces an accuracy value of 88.8%.
Work Readiness Prediction of Telkom University Students Using Multinomial Logistic Regression and Random Forest Method Haura Athaya Salka; Kemas Muslim Lhaksmana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4546

Abstract

Work readiness for college graduates is an essential and significant thing to get a job immediately after graduation. But what happens is that many graduates are unemployed after graduation or do not get jobs that match the majors they have studied for more than four years. Therefore, by using a people analytics approach, this study aims to predict the work readiness of Telkom University students and find out what factors affect student work-readiness after graduation. The model built is a multi-classes classification model. This model uses Chi-square Test calculation for feature selection, Multinomial Logistic Regression and Random Forest as a classification method, and confusion matrix as an evaluation method. Multinomial Logistic Regression is used because several studies use this algorithm for categorical data, while Random Forest is used to compare which model produces better accuracy. This study conducted several test scenarios, which obtained the best model by performing hyperparameter tuning and handling unbalanced data with SMOTE-ENN. Handling imbalanced data with SMOTE-ENN is used to improve accuracy scores and predict classes well, especially for minority class. The best accuracy of the Multinomial Logistic Regression method is 53.9%, and Random Forest is 48.5%.
Classification Analysis of Waiting Period for Telkom University Alumni to Get Jobs Using Decision Tree and Support Vector Machine Annisa Miranda; Kemas Muslim Lhaksamana
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1963

Abstract

Tracer analysis is one of the ways to increase a university's accreditation. Tracer studies, also known as graduate surveys, are beneficial for enhancing learning and developing university curricula. The period it takes graduates to secure employment is a measure of their quality. The sooner graduates obtain a job, the higher their perceived quality. Conversely, if it takes graduates longer to find employment, their quality is deemed lower. To gain new knowledge from the tracer study dataset regarding the relationship between university contribution and alumni capability in the job market, in this study, data mining techniques are used to determine what factors influence the length of time it takes college graduates to find employment. This classification model contains a total of 2288 data instances from the categorical type of dataset. The features are selected using chi-square. Two classification algorithms, Decision Tree and Support Vector Machine, are compared for the best model. This study also used hyperparameter tuning to improve accuracy. The results show decision tree produces higher accuracy compared to the support vector machine. The accuracy obtained from the decision tree model is 55.02% and increased to 65.06% after hyperparameter tuning. Meanwhile, the support vector machine brought an accuracy of 60.40% and increased to 62.15% after hyperparameter tuning. Factors that affect the classification of the alumni waiting period in getting a job in this study are sex, faculty of the study field, department of the study field, study period, company specification, company category, and work location.
Topic Classification of Quranic Verses in English Translation Using Word Centrality Measurement Achmad Salim Aiman; Kemas Muslim Lhaksmana; Jondri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4358

Abstract

Every Muslim in the world believes that the Quran is a miracle and the words of God (Kalamullah) revealed to the Prophet Muhammad SAW to be conveyed to humans. The Quran is used by humans as a guide in dealing with all problems in every aspect of life. To study the Quran, it is necessary to know what topic is being discussed in every single verse. With the help of technology, the verses of the Quran can be given topics automatically. This task is called multilabel classification where input data can be classified into one or more categories. This research aims to apply the multilabel classification to classify the topics of the Quranic verses in English translation into 10 topics using the Word Centrality measurement as the word weighting value. Then a comparison is made to the 4 classification methods, namely SVM, Naïve Bayes, KNN, and Decision Tree. The result of the centrality measurement shows that the word ‘Allah’ is the most important or the most central word of the whole document of the Quran with the scenario using stopword removal. Furthermore, the use of word centrality value as term weighting in feature extraction can improve the performance of the classification system.
Analisis Sentimen Kendaraan Listrik Pada Media Sosial Twitter Menggunakan Algoritma Logistic Regression dan Principal Component Analysis Youga Pratama; Danang Triantoro Murdiansyah; Kemas Muslim Lhaksmana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5575

Abstract

Twitter sentiment analysis is a method for identifying a person's opinions, reactions, judgments, evaluations, and emotions towards certain topics on Twitter social media. Opinions or can be called opinions can be classified as positive or negative. This research was conducted to find out public opinion about electric vehicles on Twitter social media, which is more positive or negative. The data obtained was 1874 tweets with data divided into training data and testing data at a ratio of 80:20. Data is classified using the Logistic Regression (LR) method, and Principal Component Analysis (PCA) as an optimization to improve accuracy. In this study it was found that around 86.9% of the opinions were positive, and 13.1% of the opinions were negative on the topic of electric vehicles. The results of research conducted using the Logistic Regression algorithm obtained the best accuracy of 87.9%, and after being optimized using Principal Component Analysis the best accuracy obtained increased to 90%.
Purwarupa Perangkat Iot Untuk Smart Greenhouse Berbasis Mikrokontroler Aghi Wardani; Kemas Muslim Lhaksmana
eProceedings of Engineering Vol 5, No 2 (2018): Agustus 2018
Publisher : eProceedings of Engineering

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

Abstract

Abstrak Internet of Things (IoT) merupakan suatu konsep yang bertujuan untuk memperluas manfaat dari konektivitas Internet yang tersambung secara terus menerus. IoT bisa dimanfaatkan pada greenhouse untuk mengendalikan peralatan elektronik seperti air cooler dan pompa air yang dapat dioperasikan secara otomatis. Selain itu dengan memonitoring secara real time terhadap suhu udara, kelembaban udara, kelembaban tanah, dan intensitas cahaya yang terdapat di dalam greenhouse, berbagai tanaman di dalam greenhouse dapat tumbuh dengan optimal. Sistem yang digunakan dalam tugas akhir ini menggunkan NodeMCU ESP8266 sebagai pusat kontrol dan menggunakan DHT11, Soil Moisture, dan LDR sebagai sensor untuk mengukur IoT suhu udara, kelembaban udara, kelembaban tanah, dan intensitas cahaya di dalam greenhouse. Sebagai kendali di dalam greenhouse terdapat dua output kendali, yaitu air cooler dan pompa air. NodeMCU akan membaca suhu udara, kelembaban udara, kelembaban tanah, dan intensitas cahaya yang dikirim dari modul DHT11 yang akan menentukan apakah air cooler dan pompa air akan menyala atau tidak. Semua output yang diterima oleh NodeMCU akan dikirim ke server yang sebelumnya diproses pada halaman web yang dibuat menggunakan bahasa PHP. Selanjutnya pada saat output sudah diterima pada alamat web yang dituju maka output tersebut akan dikirm ke database MySQL. Sehingga web dapat menampilkan suhu udara, kelembaban udara, kelembaban tanah, dan intensitas cahaya greenhouse secara real-time. Kata kunci : IoT (Internet of Things), Greenhouse, NodeMCU, DHT11, Kendali, Monitoring Abstract Internet of Things (IoT) is a concept that aims to expand the benefits of continuously connected Internet connectivity. IoT can be used in greenhouse to control electronic equipment such as water cooler and water pump which can be operated automatically. In addition, by real time monitoring on air temperature, air humidity, soil moisture, and light intensity contained in the greenhouse, plants in the greenhouse can grow optimally. The system used in this final project uses NodeMCU ESP8266 as the control center and uses DHT11, Soil Moisture, and LDR as sensors to measure air temperature, humidity, soil moisture, and light intensity in the greenhouse. as the control inside the greenhouse contains 2 output controls namely air cooler and water pump. NodeMCU will read air temperature, air humidity, soil moisture, and light intensity sent from DHT11 module which will determine whether water cooler and water pump will be on. All output received by NodeMCU will be sent to the server that was previously processed on web pages created using PHP language. Furthermore, when the output is received at the destination web address then the output will be sent to the MySQL database. So the Web can display air temperature, air humidity, soil moisture, and light intensity greenhouse in real-time. Keywords: IoT (Internet of Things), Greenhouse, NodeMCU, DHT11, Control, Monitoring
Analisis Dan Implementasi Algoritma Naive Bayes Classifier Terhadap Judul Berita Pemilihan Gubernur Jawa Barat 2018 Pada Media Online Resky Nadia; Kemas Muslim; Fhira Nhita
eProceedings of Engineering Vol 5, No 1 (2018): April 2018
Publisher : eProceedings of Engineering

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

Abstract

Berita mengenai pemilihan Gubernur Jawa Barat 2018 akan menjadi bahasan penting untuk dianalisis kebenarannya, dikarenakan banyaknya media online yang dalam penyebaran judul beritanya cenderung menampilkan kalimat yang mempengaruhi mindset pembacanya hanya dengan sekali lihat. Adanya judul berita yang bersifat positif ,negative atau netral. Hal tersebut dikhawatirkan dapat mengubah cara pandang masyarkat terkait suatu hal yang sebenarnya belum sesuai dengan judul yang tertera dan menarik perhatian masyarakat umum maka dari itu diperlukan klasifikasi. Makalah ini memaparkan sebuah hasil analisis dan klasifikasi yang terbagi menjadi tiga kelas yaitu positif negative dan netral, dengan step awal yaitu dengan melakukan survey kepada 5 orang untuk pemberian kelas terhadapt 5 media online yaitu Kompas.com, Detik.com, Liputan6.scom, Tribunnews.com dan Republikaonline.com. selanjutnya akan dilakukan klasifikasi menggunakan Algoritma Naïve Bayes Classifier dan akan menghasilkan sebuah akurasi dari setiap kelas dengan menganalisis 3 skenario.Dengan menggunakan Algoritma Naive Bayes maka didapatkan hasil klasifikasi dengan akurasi masing masing Detik.com dengan akurasi makro 78% dan mikro 80%, Kompas.com 48% dan48%, Liputan6.com 65% dan 65%, Tribunnews.com 65% dan 65% serta Republikaonline.com 77% dan 77%. Kata kunci : Kata Kunci : Naïve Bayes, Media Online, Pilgub Jawa Barat.
Co-Authors Abdurrahman, Azzam Achmad Salim Aiman Adelia, Dila Adhyaksa Diffa Maulana Aditya Eka Wibowo Aditya Gifhari Soenarya Adiwijaya Aghi Wardani Agni Octavia Agus Kusnayat Ahmad Syafiq Abiyyu Ahmad, Alif Faidhil Al Faraby, Said Alberi Meidharma Fadli Hulu Amalia Elma Sari Andiani, Annisa Dwi Angraini, Nadya Arda Anisa Herdiani Annisa Miranda Arini Rohmawati Athallah, Muhammad Rafi Aura Sukma Andini Bayu Muhammad Iqbal Bonar Panjaitan Brata Mas Pintoko Chandra Jaya Riadi Chlaudiah Julinar Soplero Lelywiary Choirulfikri, Muhammad Rizqi Damayanti, Lisyana Dana Sulitstyo Kusumo Danang Triantoro Murdiansyah David Winalda Delva, Dwina Sarah Deni Saepudin Denny Darlis Dewantara, Muhammad Pascal Dida Diah Damayanti Didit Adytia dina juni restina Dino Caesaron Donni Richasdy Donny Rhomanzah Dzidny, Dimitri Irfan Edgarsa Bramandyo Widyarto Eki Rifaldi Eko Darwiyanto Ela Nadila Emrald Emrald Erwin Budi Setiawan Fakhrana Kurnia Sutrisno Farisi, Kamaludin Hanif Fathih Adawi Ahmad Ferdian Yulianto Fhira Nhita Ghina Annisa Shabrina Guido Tamara Haga Simada Ginting Harmandini, Keisha Priya Haura Athaya Salka Herodion Simorangkir Hutama, Nanda Yonda Ika Puspita Dewi Intan Khairunnisa Fitriani Iqmal Lendra Faisal Amien Irgi Aditya Rachman Isabella Vichita Kacaribu Isman Kurniawan Jofardho Adlinnas Jondri Jondri Jordan, Brilliant Kamaludin Hanif Farisi Kautsar Ramadhan Sugiharto Lukito Agung Waskito Luqman Bramantyo Rahmadi Luthfi, Muhammad Faris M. Mahfi Nurandi Karsana Mahendra Dwifebri Mahendra Dwifebri Purbolaksono Mahendra, Muhammad Hafizh Marendra Septianta Marozi, Ericho Mehdi Mursalat Ismail Meira Reynita Putri Mira Rahayu Moch Arif Bijaksana Mohamad Reza Syahziar Muhammad Abdurrohman Al Fatih Muhammad Adzhar Amrullah Muhammad Arif Kurniawan Muhammad Ilham Maulana Muhammad Rifqi Fauzi Ramdhani Muhammad Yuslan Abu Bakar Muhammad Zaid Dzulfikar muhammad zaky ramadhan Muhammad Zidny Naf'an Murman Dwi Praseti Musyafa’noer Sandi Pratama Nanda Yonda Hutama Naufal Furqan Hardifa Naufal Hilmiaji Naufal Rasyad Nibras Syihabil Haq Octaryo Sakti Yudha Prakasa Okky Zoellanda A. Tane Pamungkas, Danit Hafiz Praja, Yudhistira Imam Purwita, Naila Iffah Putri, Arla Sifhana Putrisia, Denada R. Fajrika hadnis Putra Rafi Hafizhni Anggia Rafisa Arif Irfan Rahadian, Muhammad Rafi Rastim Rastim Rayhan, Muhammad Aditya Resky Nadia Rizki Luthfan Azhari Rizki Nurhaliza Harahap Rizky Ahmad Saputra Rizky, Fariz Muhammad Salman Farisi Setya Hadi Seno Adi Putra Seto Sumargo Siddiq, Ikhsan Maulana Sindi Fatika Sari Sri Utami Sri Widowati Sukmawan Pradika Janusange Santoso Suwaldi Mardana Syadzily , Muhammad Hasan Tri Widarmanti Try Moloharto Try Moloharto Vitalis Emanuel Setiawan Wardhani, Fitri Herinda Widi Astuti Widi Astuti Youga Pratama Yuliant Sibaroni Yusuf Nugroho Doyo Yekti Zaena, Siffa Zaenal Abidin ZK Abdurahman Baizal Zulkarnaen, Imran