Article Per Year (5 Year)
p-Index From 2020 - 2025
4.839
P-Index
This Author published in this journals
All Journal Jurnal Teknik Industri Syntax Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika Jurnal Inspiration Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Cyberspace: Jurnal Pendidikan Teknologi Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) Community Development Journal: Jurnal Pengabdian Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika)
Merinda Lestandy
Universitas Muhammadiyah Malang
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Public Health Other

Published : 27 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 27 Documents
Search

 1   2   3 
PEMBERDAYAAN KELOMPOK MASYARAKAT GOTONG ROYONG SEJAHTERA DALAM PENGEMBANGAN PRODUKSI KERIPIK UBI DAN KACANG GORENG DI DESA KROMENGAN Merinda Lestandy; Lailis Syafaah; M. Irfan
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 3 No. 3 (2022): Volume 3 Nomor 3 Tahun 2022
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v3i3.7540

Abstract

Berdasarkan sumber Dinas Pemberdayaan Masyarakat dan Desa Kabupaten Malang, Kecamatan Kromengan memiliki kelompok PKK sebanyak 16 kelompok. Kelompok PKK tersebut mayoritas berisi ibu rumah tangga meskipun ada beberapa ibu yang bekerja. Hal ini menunjukkan bahwa masih ada penduduk Kecamatan Kromengan yang belum bekerja. Sehingga ibu-ibu rumah tangga adalah kelompok yang memungkinkan untuk diberdayakan dengan baik, maka diharapakan ibu-ibu rumah tangga ini mampu menghasilkan suatu produk yang memiliki nilai ekonomis sehingga mampu meningkatkan ekonomi keluarganya. Keberadaan ibu-ibu ini merupakan salah satu potensi untuk bisa mengembangkan Kelompok Masyarakat “Gotong Royong Sejahtera”. Kelompok masyarakat Gotong Royong Sejahtera memproduksi makanan ringan seperti keripik talas dan kacang goreng. Kelompok masyarakat ini memiliki kendala dalam proses pembuatan talas dan gorengan kacang, kemasan dan pemasaran yang kurang menarik, namun masih menyerahkan penjualan kepada pengusaha lokal. Oleh karena itu, layanan ini bertujuan untuk memberikan solusi atas permasalahan tersebut dengan memberikan bantuan peralatan gorengan atau alat sangrai makanan khususnya kacang dan keripik talas. Pengabdi telah melakukan pengabdian dan memberikan alat yang membantu meningkatkan hasil produksi yang berkualitas sehingga dapat meningkatkan pendapatan mitra.
PENINGKATAN KOMPETENSI GURU TK ABA 16 MALANG MELALUI PELATIHAN DAN PENDAMPINGAN DI BIDANG TIK (TEKNOLOGI INFORMASI DAN KOMPUTER) Merinda Lestandy; Lailis Syafaah; Amrul Faruq
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 3 No. 3 (2022): Volume 3 Nomor 3 Tahun 2022
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v3i3.7543

Abstract

Pemanfaatan teknologi dalam kehidupan sehari-hari tidak dapat dihindari. Perkembangan teknologi pada era globalisasi saat ini sangat pesat. Untuk itu, masyarakat dituntut untuk melakukan suatu perubahan di setiap kegiatannya. Terutama bagi para guru diharapkan dapat mengikuti perubahan tersebut dalam meningkatkan kualitas kegiatan belajar mengajar. Perkembangan Teknologi Informasi dan Komunikasi (TIK) telah berpengaruh terhadap berbagai aspek kehidupan manusia. Hal ini mendorong era baru peradaban manusia dari era industri ke era informasi. Berdasarkan hasil wawancara dan observasi pada TK ABA 16 Malang kami melihat masih ada guru yang beranggapan tidak menggunakan komputer dan IT (Teknologi Informasi) dalam proses pembelajaran bukan hal mengganggu jalannnya pelajaran karena guru merasa tidak mendapatkan fasilitas komputer saat mengajar, jadi inilah yang membuat guru merasa tidak perlu untuk tahu cara menggunakan komputer. Jika dilihat dari kenyataannya ini terjadi pada guru-guru yang sudah berusia tua, walaupun yang guru junior pun masih ada yang gagap pada kemajuan IT. Dari permasalahan tersebut, maka perlu diadakan kegiatan pelatihan dan pemahaman bagi guru untuk mengoptimalkan fasilitas TIK yang ada untuk menunjang keefektifan pembelajaran dan kompetensi guru. Setelah pelaksanaan pelatihan, tim pengusul akan memonitoring dan mengevaluasi pemahaman guru-guru tentang TIK yang ditentukan dengan standar keberhasilan pemahaman yaitu pencapaian skor harapan senilai 7 dari jumlah skor hasil observasi. Dari skor tersebut maka dapat disimpulkan bahwa guru sudah meningkat kompetensinya atau belum setelah mengikuti pelatihan IT mengenai penggunaan TIK sebagai media mengajar. Pelaksanaan evaluasi dilakukan dengan mengamati peningkatan kompetensi guru setelah satu minggu mengajar menggunakan IT melalui lembar observasi.
Penerapan Synthetic Minority Oversampling Technique (SMOTE) untuk Imbalance Class pada Data Text Menggunakan kNN Sultan Maula Chamzah; Merinda Lestandy; Nur Kasan; Adhi Nugraha
SYNTAX Jurnal Informatika Vol 11 No 02 (2022): Oktober 2022
Publisher : Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/syji.v11i02.6940

Abstract

Tokopedia is one of the online marketplace providers in Indonesia that facilitates internet users to buy and sell online. Tokopedia gets an average of 147.79 million website and application visitors per month. Although it has many users, of course in an application it has advantages and disadvantages. This was conveyed by users through reviews or reviews contained in the Google Play Store. In the review, it can be seen that more users who gave 5-star rating reviews than users gave 1 star rating. The Synthetic Minority Oversampling Technique or SMOTE is a popular method applied in order to deal with class imbalances. This study aims to determine the performance of the K-Nearest Neighbor algorithm in dealing with imbalance class using Synthetic Minority Oversampling Technique (SMOTE). This study uses 5000 data consisting of 3975 negative data and 1025 positive data. Of the 5000 data divided into two parts, 70% training data and 30% test data. The SMOTE-kNN method shows a better accuracy result, which is 90% compared to using only kNN with an accuracy value of 82%.
Emotional Text Classification Using TF-IDF (Term Frequency-Inverse Document Frequency) And LSTM (Long Short-Term Memory) Muhammad Ibnu Alfarizi; Lailis Syafaah; Merinda Lestandy
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (901.31 KB) | DOI: 10.30595/juita.v10i2.13262

Abstract

Humans in carrying out communication activities can express their feelings either verbally or non-verbally. Verbal communication can be in the form of oral or written communication. A person's feelings or emotions can usually be seen by their behavior, tone of voice, and expression. Not everyone can see emotion only through writing, whether in the form of words, sentences, or paragraphs. Therefore, a classification system is needed to help someone determine the emotions contained in a piece of writing. The novelty of this study is a development of previous research using a similar method, namely LSTM but improved on the word weighting process using the TF-IDF method as a further process of LSTM classification. The method proposed in this research is called Natural Language Processing (NLP). The purpose of this study was to compare the classification method with the LSTM (Long Short-Term Memory) model by adding the word weighting TF-IDF (Term Frequency–Inverse Document Frequency) and the LinearSVC model, as well to increase accuracy in determining an emotion (sadness, anger, fear, love, joy, and surprise) contained in the text. The dataset used is 18000, which is divided into 16000 training data and 2000 test data with 6 classifications of emotion classes, namely sadness, anger, fear, love, joy, and surprise. The results of the classification accuracy of emotions using the LSTM method yielded a 97.50% accuracy while using the LinearSVC method resulted in an accuracy value of 89%.
The Utilization of Deep Learning in Forecasting The Inflation Rate of Education Costs in Malang Afrah, Ashri Shabrina; Lestandy, Merinda; Suwondo, Juwita P. R.
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 7 No. 1 (2023)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v7i1.729

Abstract

The public needs information about the predicted inflation rate for education costs to manage family finances and prepare education funds. This information is also beneficial for the government to create policies in education. Malang is one of the educational cities in Indonesia, but research on the prediction of the inflation rate of education costs in the city still needs to be made available. Besides, the researchers have yet to find previous studies on forecasting that used the specific inflation rate for education costs in Indonesia by applying the Deep Learning method, especially those using the Consumer Price Index (CPI) data for the Education Expenditure Group. This research aims to develop a model to forecast the inflation of education costs in Malang using the Deep Learning Method. This research was conducted using Consumer Price Index (CPI) data for the Education Expenditure Group in Malang during 1996-2021s taken from the Central Bureau of Statistics (BPS) Malang. The forecasting method used is the Long and Short-Term Memory (LSTM) method, which is a development of the Recurrent Neural Network (RNN). The results showed that it achieved the best accuracy by a model with one hidden layer and four hidden nodes, namely MAPE=2.8765% and RMSE=8.37.
APLIKASI SISTEM INFORMASI SEBAGAI UPAYA PENINGKATAN MANAJEMEN DAN PRODUKTIVITAS DALAM EKONOMI DIGITAL PADA PRODUK Q-MAS M PT. TASAMA Merinda Lestandy; Khusnul Hidayat; Shanty Kusuma Dewi; Lukman Hakim; Muhammad Aqmal; Dzaky Darrell Bayanaka; Pranadi Sigit Dwi Laksono; Bima Septian Adji
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 6 (2024): Vol. 5 No. 6 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i6.36537

Abstract

Program Pengabdian kepada Masyarakat ini berawal dari kebutuhan PT. TASAMA untuk memperkuat manajemen dan produktivitas dalam era ekonomi digital, khususnya terkait pengelolaan produksi dan kualitas produk air minum Q-Mas M. Sistem manual yang sebelumnya digunakan dalam pencatatan produksi dan pemantauan kualitas air dianggap kurang efisien dan rentan terhadap kesalahan. Oleh karena itu, diperlukan solusi berbasis teknologi informasi yang dapat mengoptimalkan proses produksi serta menjaga dan memantau kualitas air minum dengan lebih akurat dan real-time. Tujuan utama dari program ini adalah mengembangkan serta menerapkan aplikasi sistem informasi berbasis web yang dapat mencatat data produksi dan memonitor kualitas air minum secara real-time. Diharapkan, aplikasi ini mampu meningkatkan efisiensi manajemen, transparansi dalam pencatatan, serta memudahkan pengawasan kualitas produk PT. TASAMA, sehingga memperkuat daya saing perusahaan dalam ekonomi digital. Kegiatan pengabdian kepada masyarakat ini dilakukan melalui beberapa tahapan. Pertama, dilakukan identifikasi kebutuhan mitra untuk memahami kendala dan kebutuhan spesifik PT. TASAMA. Langkah ini diikuti dengan perencanaan sistem, yang mencakup desain aplikasi dan fitur-fitur yang dibutuhkan. Pada tahap implementasi, aplikasi berbasis web dikembangkan dan diterapkan untuk pencatatan produksi, serta pemasangan alat monitoring kualitas air minum secara real-time. Setelah sistem terpasang, karyawan PT. TASAMA menerima pelatihan agar mereka dapat mengoperasikan sistem dengan lancar. Tahap akhir adalah monitoring dan evaluasi untuk memastikan efektivitas dan keberlanjutan sistem yang telah diterapkan. Luaran yang diharapkan dari kegiatan ini meliputi aplikasi pencatatan data produksi berbasis web, sistem pemantauan kualitas air minum secara real-time, serta peningkatan kapasitas sumber daya manusia melalui pelatihan. Hasil dari program pengabdian ini menunjukkan penerapan teknologi informasi yang berhasil dalam meningkatkan efisiensi pencatatan dan monitoring. Karyawan PT. TASAMA juga telah dilatih untuk mengoperasikan sistem secara mandiri. Dengan adanya sistem ini, pengelolaan produksi dan kualitas air minum berjalan lebih optimal, sehingga PT. TASAMA mengalami peningkatan manajemen dan produktivitas yang signifikan di era ekonomi digital.
Buck-boost Converter using GA-based MPPT for Solar Energy Optimization Syafaah, Lailis; Faruq, Amrul; Noor Cahyadi, Basri; Hidayat, Khusnul; Setyawan, Novendra; Lestandy, Merinda; Zulfatman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 3, August 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i3.1658

Abstract

Energy optimization in the Solar Power Plant system needs to have more attention. Indonesia is a tropical country that has two seasons, where the weather and cloud movements are frequently unpredictable, especially in the southern region of Java Island. To overcome this problem, an inverter equipped with maximum power point tracking (MPPT) was used. However, the current MPPT switching system was still not optimal with an efficiency of around 90%. In this study, the installation of MPPT was carried out in order to optimize the power in solar photovoltaic (PV) system due to the fluctuations of solar irradiation at PT. Jatinom Indah Agri, Blitar City. The maximum power generated by solar photovoltaic could be achieved by using the combination of DC - DC converter and artificial intelligence. In this study, the modeling of solar PV system was made using MATLAB software, where the design of the solar PV system consisted of a PV module with capacity 240W, DC to DC converter, battery and MPPT. Genetic Algorithm (GA)-based MPPT had been tested and compared to Particle Swarm Optimization (PSO)-based MPPT and conventional MPPT, where the GA-based MPPT worked well in finding the maximum power point in the solar photovoltaic system. It was found that GA-based MPPT produced a maximum power point close to PV power with an efficiency of 92%, while the effciciency of PSO-based MPPT and conventional MPPT were 85% and 79% respectively. In selecting the method for designing MPPT, a method with a wide range of sample data is required. This is due to the fluctuation of solar irradiance received by the solar PV.
Penerapan Deep Learning untuk Prediksi Kasus Aktif Covid-19 Syafa’ah, Lailis; Lestandy, Merinda
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.337

Abstract

Coronavirus disease (Covid-19) is increasingly spreading in Indonesia, so it requires an approach to predict its spread. One approach method that is often used is the Deep Learning (DL) method. DL is a branch of Machine Learning (ML) which is modeled based on the human nervous system. In this study, the prediction of active Covid-19 cases was resolved using the DL method. The dataset used is 260 data with 10 parameters. DL is able to provide an accurate prediction of active cases of Covid-19 with an MSE of 0.032 and an accuracy of 81.333%.
Penerapan Deep Learning untuk Prediksi Kasus Aktif Covid-19 Syafa’ah, Lailis; Lestandy, Merinda
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (896.278 KB) | DOI: 10.30645/j-sakti.v5i1.337

Abstract

Coronavirus disease (Covid-19) is increasingly spreading in Indonesia, so it requires an approach to predict its spread. One approach method that is often used is the Deep Learning (DL) method. DL is a branch of Machine Learning (ML) which is modeled based on the human nervous system. In this study, the prediction of active Covid-19 cases was resolved using the DL method. The dataset used is 260 data with 10 parameters. DL is able to provide an accurate prediction of active cases of Covid-19 with an MSE of 0.032 and an accuracy of 81.333%.
Ensembled Machine Learning Methods and Feature Extraction Approaches for Suicide-Related Social Media Merinda Lestandy; Abdurrahim Abdurrahim; Amrul Faruq; M. Irfan; Novendra Setyawan
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.70016

Abstract

Suicide is a pressing public health concern that affects both young people and adults. The widespread use of mobile devices and social networking has facilitated the gathering of data, allowing academics to assess patterns, concepts, emotions, and opinions expressed on these platforms. This study is to detect suicidal inclinations using Reddit online dataset. It allows for the identification of people who express thoughts of suicide by analyzing their postings. The method addresses and evaluates different machine learning classification models, namely linear SVC, random forest, and ensemble learning, along with feature extraction approaches such as TF-IDF, Bag of Words, and VADER.   This study utilised a voting classifier in our ensemble model, where the projected class output is selected by the class with the highest probability. This approach, typically known as a "voting classifier," employs voting to forecast results. The results collected suggest that employing ensemble learning with the TF-IDF 2-grams approach yields the highest F1-score, specifically 0.9315. The efficacy of TF-IDF 2-grams can be determined to their capacity to capture a greater amount of contextual information and maintain the order of words.
Co-Authors Abdurrahim Abdurrahim Abdurrahim Abdurrahim Achmad Nurdim Fikri Adhi Nugraha Adhi Nugraha Adhi Nugraha Ahmad Ihsan Fauzi Alfina Fatma Damayanti Alvian Widianto Amrul Faruq Amrul Faruq Amrul Faruq Anggia Shifrila Hamidah Ashri Shabrina Afrah Bima Septian Adji Catur Diah Rochmad Dwi Nur R Dwi Nur Rachmawati Dwiko Putra Dzaky Darrell Bayanaka Ermanu Azizul Hakim Fikri Hakim Soultoni Irfan, Muhammad Khaeruddin Khaeruddin Khusnul Hidayat La Febry ARC Lailis Syafa'ah Lailis Syafa’ah Lailis Syafa’ah Lukman Hakim M. Irfan M. Irfan M. Irfan M. Irfan M. Nasar Machmud Effendy Mochammad Arif Hidayatullah Mohammad Chasrun Hasani Muhammad Aqmal Muhammad Aswin Muhammad Bara Al Farisyi Muhammad Ibnu Alfarizi Noor Cahyadi, Basri Novendra Setyawan Nur Alif Mardiyah, Nur Alif Nur Kasan, Nur Nurhadi Nurhadi Nurhadi, Nurhadi Pranadi Sigit Dwi Laksono Raaina Arleandy Putri Selfi Rizkianto Shanty Kusuma Dewi Sultan Maula Chamzah Suwondo, Juwita P. R. Syafaah, Lailis Syafaah, Lailis Syafa’ah, Lailis Yoga Nur Fimansyah Yoga Nur Firmansyah Zulfatman Zulfatman