Article Per Year (5 Year)
p-Index From 2020 - 2025
2.304
P-Index
This Author published in this journals
All Journal Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization JOURNAL OF APPLIED INFORMATICS AND COMPUTING SEIKO : Journal of Management & Business Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Buletin Poltanesa Inovasi Teknologi Masyarakat
Heliza Rahmania Hatta, Heliza Rahmania
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Land Suitability for Mustard Plants Using Multi-Objective Optimization by Ratio Analysis Method Hatta, Heliza Rahmania; Ariani, Riska; Khairina, Dyna Marisa; Maharani, Septya; Kamila, Vina Zahrotun; Wijayanti, Arini
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1290

Abstract

Sawi dapat dikembangkan atau dikembangkan dari sudut pandang finansial dan bisnis untuk memenuhi permintaan pembeli dan menangkap peluang pasar yang signifikan. Sawi merupakan tanaman hortikultura yang mempunyai daya adaptasi tinggi dan waktu panen yang relatif singkat. Sawi ini menawarkan banyak keuntungan bagi petani. Misalnya saja banyak petani yang menanam sawi di Samarinda, Kalimantan Timur, Indonesia. Meskipun sangat mudah beradaptasi, beberapa spesies sawi tidak tumbuh subur di tanah tertentu. Tanah yang baik sangat penting untuk hasil optimal saat menanam sawi. Sawi yang ditanam dapat diseleksi dengan menggunakan pendukung keputusan berdasarkan kriteria lahan untuk mendapatkan hasil terbaik. Tujuan dari penelitian ini adalah untuk merekomendasikan tanaman sawi yang cocok berdasarkan kebutuhan luas dengan menggunakan pendekatan multi-objective optimize by ratio analysis (MOORA). MOORA merupakan suatu metode pengambilan keputusan yang membantu dalam memilih alternatif terbaik dari beberapa pilihan atau alternatif berdasarkan beberapa kriteria atau tujuan. Pengamatan ini menggunakan lima kriteria yaitu jenis tanah, pH tanah, curah hujan, suhu, ketinggian lokasi, dan enam alternatif sawi. Berdasarkan uji lahan, sawi yang direkomendasikan metode MOORA adalah Sawi Sendok atau Pak Choy dengan nilai Yi sebesar 7,6698. Jadi yang dipilih sebagai sawi yang ditanam di lahan tersebut adalah Sawi Sendok atau Pak Choy. Untuk penelitian selanjutnya perlu dilakukan penambahan atau penyesuaian kriteria dan sensor baru secara real-time yang dapat diterapkan untuk meningkatkan efisiensi sawi menuju smart farming yang fokus pada hasil yang lebih baik dengan tetap menjaga keseimbangan alam.
Grade Classification of Agarwood Sapwood Using Deep Learning Hatta, Heliza Rahmania; Nurdiati, Sri; Hermadi, Irman; Turjaman, Maman
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2257

Abstract

The agarwood tree (Aquilaria sp.) is a tree that produces agarwood, which is a black resin that has a distinctive fragrant smell. In Indonesia, one that is commonly traded is sapwood agarwood. Agarwood sapwood is black or brownish-black wood obtained from the parts of the agarwood-producing tree containing a strong aromatic mastic. Based on the Indonesian National Standard (SNI) 7631:2018, agarwood sapwood has three classes: Super Double, Super A, and Super B. However, many agarwood farmers need to learn to differentiate and classify the agarwood sapwood classes, and traders exploit this to buy cheap. So, deep learning can be used to classify the agarwood sapwood class. One of the uses of deep learning is in image processing. Image processing is used to help humans recognize or classify objects quickly and precisely and can process many data simultaneously. One of the deep learning algorithms used in image processing is the Convolutional Neural Network (CNN). In this study, it is proposed that the deep learning model used is CNN with batch normalization. The dataset used is 72 agarwood sapwood images with a white background, each consisting of 24 Super A, 24 Super B data, and 24 Super Double data. The dataset is divided into 80% training and 20% testing data. The evaluation results of the proposed method at 100 epochs show an accuracy of 87.5%. The research implications will help agarwood tree farmers differentiate and classify agarwood sapwood so that farmers get the right price from buyers.
Diagnosis of Diseases in Rubber Stems Using the Dempster Shafer Method Sukmono, Yudi; Pratiwi, Sinthya Ayu; Hatta, Heliza Rahmania; Septiarini, Anindita; Padmo Azam Masa, Amin; Wijayanti, Arini
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3474

Abstract

Rubber (Hevea Brasiliensis) is a non-timber forest product originating from the Americas and is currently widely distributed worldwide, including in East Kalimantan, Indonesia. In their management in East Kalimantan, farmers often encounter diseases in rubber plants, especially diseases of the stems, which can cause plant death. This disease requires treatment, but if it is too severe, it can harm farmers economically and in production, so it is essential for farmers to recognize the symptoms of this disease early from changes in the rubber plant stems. This study aims to diagnose diseases of rubber stems using the Dempster Shafer method. Dempster Shafer is a relevant method for overcoming the uncertainty of symptoms and rules, enabling expert systems to generate conclusions with certainty. This method has advantages in solving various problems and simultaneously combining evidence (facts) from several sources. This research was conducted by analyzing a dataset of 80 data, covering 7 types of diseases and 27 different symptoms. The accuracy test results show that the research has an accuracy rate of 96.25%. The implications of this research are significant. It is hoped that it can significantly help rubber plantation farmers in East Kalimantan and also make a valuable contribution to agricultural and plantation extension agents in overcoming the challenges faced due to diseases in rubber plant stems. Thus, this research could increase the productivity and sustainability of the rubber plantation sector in this region.
Data Mining Untuk Estimasi Sidang Perkara Narkotika Menggunakan Metode Regresi Linier Berganda Khairina, Dyna Marisa; Shapanara, Rhenaldi Octa; Maharani, Septya; Hatta, Heliza Rahmania
Journal of Applied Informatics and Computing Vol. 6 No. 2 (2022): December 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i2.4401

Abstract

Narcotics cause unrest in the community because it has a very bad impact on society. The number of reports of narcotics cases has an impact on the number of executions in the trial of these cases. From the number of trial executions, it is necessary to follow up efforts to anticipate the handling of narcotics cases by knowing in advance the trend/pattern of increasing/decreasing narcotics cases as supporting information in efforts to handle these cases. The purpose of the research is to help speed up the process of calculating and managing the information contained in the data into new knowledge so that an estimate of the trial of narcotics cases is produced based on information on the pattern/trend of increasing/decreasing narcotics. The case uses multiple linear regression which is then tested for the coefficient of determination and the simultaneous significant test. The case data used is a time series from January 2021 to December 2021. The resulting regression model is Y = 39.777 "“ 0.035 X1 "“ 0.065 X2. The calculation of the regression results shows that the estimation of the implementation of the number of stages of narcotics cases with stage I and stage II variables has a negative effect on the implementation of narcotics cases based on the results of hypothesis testing conducted.
Workload, Emotional Intelligence, And Intellectual Intelligence On Employee Performance At Maranatha Christian University Rahayu, Puspita Puji; Santosa, Sonny; Sampe, Ferdinandus; Hatta, Heliza Rahmania
SEIKO : Journal of Management & Business Vol 6, No 1 (2023): January - Juny
Publisher : Program Pascasarjana STIE Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/sejaman.v6i1.3969

Abstract

Maranatha Christian University is a university that is currently growing with a large number of employees. So it is necessary to evaluate the four factors. The purpose of this study was to evaluate the relationship between workload, intellectual intelligence, and emotional intelligence on employee performance. The results of the research will be used as a basis for determining the Institute's decisions in the placement of employees and balancing the workload in related departments. The results of the research will be used as material for study in decision making and mapping of human resources according to the performance of each employee. The research was carried out using a quantitative approach with a data collection tool using a questionnaire on 81 respondents carried out randomly (random sampling). Keywoard: Workload, Performance, Emotional intelligence, Intellectual Intelligence,Correlation.
Comparison of FMADM TOPSIS and FMADM WP in Determining Recipients of the Family Hope Program (PKH) Assistance Puspitasari, Novianti; Kurniati, Wendy; Hatta, Heliza Rahmania
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9852

Abstract

Fuzzy Multi-Attribute Decision Making (FMADM) TOPSIS and WP methods are frequently employed to identify potential recipients of government assistance. The Family Hope Program (PKH) is a government social assistance program designed to improve the welfare of underprivileged individuals. However, the process of distributing this assistance often faces obstacles in the form of inaccuracy in determining recipients. This study compares FMADM TOPSIS and WP to evaluate their effectiveness in objectively determining potential PKH recipients. The criteria for potential PKH recipients are eleven criteria obtained from the social service based on government regulations and PKH assistants. Meanwhile, the alternatives for this study are fifty samples of family data for potential PKH recipients. This study employs a sensitivity test method to assess the accuracy of the results obtained from each method. The results of the study show that FMADM TOPSIS produces a higher level of accuracy of 94% compared to FMADM WP. This study is expected to be able to contribute to choosing the right decision-making method to determine potential recipients of social assistance.
Sistem Pendukung Keputusan Pemilihan Pramuka Pandega Berprestasi Menggunakan Metode Multi Objective Optimization on the Basis of Ratio Analysis Ramadiani, Ramadiani -; Rani, Famylia Puspa; Khairina, Dyna Marisa; Hatta, Heliza Rahmania
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 2: April 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2875.236 KB) | DOI: 10.25126/jtiik.2019621284

Abstract

AbstrakPemilihan Pramuka Pandega berprestasi, merupakan kegiatan rutin setiap tahun yang dilakukan oleh Pengurus Daerah Gerakan Pramuka Provinsi Kalimantan Timur. Kegiatan ini bertujuan untuk memilih wakil Pandega yang akan dikirim ke Jakarta. Pemilihan Pramuka Pandega yang terbaik didasarkan pada kriteria usia, Indeks Prestasi Kumulatif, jumlah perkemahan wirakarya yang pernah diikuti, kepribadian dan penampilan, peluang kerja yang dimiliki dan pretasi lain serta karya tulis. Namun dengan cara penilaian yang masih manual, hal ini dianggap cukup sulit, kurang produktif, subjektif dan lambat bagi tim penilai. Sistem Pendukung Keputusan diperlukan untuk memudahkan pekerjaan tim penilai dalam memutuskan siapa yang layak dipilih sebagai Pandega berprestasi secara mudah, objektif, professional dan transparan. Pemilihan Pandega berprestasi menggunakan metode Multi Objective Optimization on the Basis of Ratio Analysis, studi literatur dan wawancara pada tim penilai, serta metode waterfall untuk tahapan pengembangan sistemnya. Berdasarkan hasil dari beberapa penelitian, metode MOORA dipilih karena memiliki nilai cost dan benefit dalam menentukan keputusan. Penelitian ini telah menghasilkan rekomendasi untuk pemilihan Pandega berprestasi dengan hasil akurasi sebesar 100%.  AbstractSelection of Scouts Pandega achievers are an annual agenda organized by the Regional Board of the Scout Movement of East Kalimantan Province. This activity aims to elect Pandega representatives to be sent to Jakarta. The best selection of Pandega Scouts is based on the criteria of age, Grade Point Average, number of workshops that have been attended, personality and appearance, work opportunities that are owned and other achievements and writing. However, with calculations that have not been automated, this is considered quite difficult, not professional, not objective and slow for the assessment team. DSS is provided to help the assessment team complete their work professionally in deciding who is eligible to be chosen as Pandega to achieve easily, objectively, professionally and transparently. The selection of Pandega achieves using the MOORA method, literature study and interviews with the assessment team, and the waterfall method for the development stages of the system. Based on the results of several studies, the MOORA method is recommended because this method allows an assessment of the costs and benefits in the final decision. The final report of this study resulted in Pandega's achievement achievement based on 100% accuracy value
Model Pengambilan Keputusan Pemilihan Bibit Unggul Sapi Bali Menggunakan Metode Weighted Product Khairina, Dyna Marisa; Pramukti, Indra Cahya; Hatta, Heliza Rahmania; Maharani, Septya
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021853512

Abstract

Kesulitan dalam mencari bibit unggul pada ternak sapi bali, menyebabkan bibit unggul yang terpilih semakin tidak produktif dalam hal penggemukan ternak. Penentuan bibit unggul pada ternak sapi bali merupakan hal yang sangat krusial bagi para pengambil keputusan yang terkait dalam hal ini adalah peternak sapi bali. Jika tidak dilakukan secara tepat dan akurat, maka pemilihan bibit unggul  pada sapi bali yang keliru seringkali mengakibatkan berbagai permasalahan. Model pengambilan keputusan dapat digunakan untuk membantu manusia khususnya peternak sapi dalam mengambil keputusan. Metode Weighted Product adalah metode yang sangat efektif dan efisien dalam pemilihan bibit unggul, karena waktu yang diperlukan untuk perhitungan jauh lebih singkat. Tujuan penelitian ini adalah membuat suatu model pengambilan keputusan untuk pemilihan bibit unggul terbaik pada ternak sapi bali. Adapun model pengambilan keputusan ini membantu memberikan rekomendasi kepada peternak dalam proses pemilihan bibit unggul sapi bali sebagai bahan pertimbangan dalam memilih secara tepat, akurat dan mempermudah proses pemilihan dengan keputusan terbaik. AbstractThe difficulty to look for superior seeds of Bali cattle causes the selected superior germ plasm being more unproductive in case of fattening cattle. The decision of superior seeds of Bali cattle is a crucial thing for the decision maker, related with this case is Bali cattle breeder. If it is not organized accurately, then the selection of superior seeds on the wrong bali cows often lead to various problems. Decision-making models can be used to help humans, especially cattle ranchers in making decisions. Weighted Product Method is a very effective and efficient method for selecting superior seeds, because the timing needed for calculation is much shorter. The purpose of this research is to make a model of decision making for selection superior seeds of Bali cattle. The decision-making model helps provide recommendations to farmers in the process of selecting superior bali cattle seeds as a material consideration in choosing the right, accurate and simplify the selection process with the best decision.
Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Indonesian Crude Oil Price Wati, Masna; Haviluddin, Haviluddin; Masyudi, Akhmad; Septiarini, Anindita; Hatta, Heliza Rahmania
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.22286

Abstract

Crude oil is the main commodity of the global economy because oil is used as an ingredient for many industries globally and is the price base used in the state budget. Indonesian Crude Price (ICP) fluctuates following developments in world crude oil prices. A significant increase in crude oil prices will certainly disrupt the economy. Thus, the movement or fluctuation of ICP is essential for business players in the energy market, especially domestically. Therefore, crude oil price forecasting is needed to assist business people in making decisions related to the energy market. This study aims to find a suitable forecasting model for Indonesian crude oil prices using the Autoregressive Integrated Moving Average (ARIMA) method. The forecasting process used ICP time-series data per month for 50 types of crude oil within five years or 63 months. Based on the experimental results, it was found that the most fit ARIMA models were (0,1,1), (1,1,0), (0,1,0), and (1,2,1). The test results for April to September 2020 have a good and proper interpretation, except the type of BRC oil indicates inaccurate forecasts. The ARIMA error rate is very dependent on the value of the data before it is predicted and external factors, the more unstable the data value every month, the higher the error rate.
Sistem Pendukung Keputusan Pemilihan Tanaman Hias Terbaik Untuk di Dalam Ruangan Menggunakan Metode Simple Additive Weighting (SAW) Jundillah, Muhammad Labib; Ramadiani; Hatta, Heliza Rahmania; S, Nadia Christin Borneo
Poltanesa Vol 23 No 1 (2022): Juni 2022
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v23i1.1286

Abstract

Tanaman hias merupakan tanaman yang sangat diminati belakangan ini karena nilai keindahan dan daya tariknya. Tanaman hias merupakan aspek yang penting karena mampu menjaga kesehatan lingkungan, semakin banyak tanaman hias semakin bagus juga untuk memperindah lingkungan. Tanaman yang terdapat di dalam ruangan dapat meningkatkan kualitas udara dan kenyaman udara, sering sekali terjadi kesalahan dalam pemilihan tanaman hias sehingga dapat menyebabkan ruangan terlihat kumuh di penuhi hewan seperti nyamuk. Oleh karena itu dalam melakukan pemilihan tanaman hias yang terbaik untuk di dalam ruangan harus memperhatikan kriteria berdasarkan faktor kualitas tanaman hias. Perlunya sistem pendukung keputusan untuk mempermudah dalam pemilihan tanaman hias secara cepat dan tepat sesuai dengan kriteria yang ada. Metode yang digunakan dalam penelitian ini yaitu metode Simple Additive Weighting (SAW) untuk melakukan perangkingan alternatif. Kriteria yang digunakan sebanyak 6 yaitu ukuran tanaman, daya tahan, pencahayaan, harga, media tanam, warna dan perawatan, sedangkan alternatif yang digunakan sebanyak 20 jenis tanaman hias yang ada di Toko Bunga Taman Puri Indah Kota Samarinda sebagai tempat penelitian. Implementasi dengan metode SAW menghasilkan rekomendasi tanaman hias yaitu Sansevieria Cylindrica dengan nilai preferensi 0,83 merupakan nilai tertinggi dibandingkan dengan alternatif lainnya.
Co-Authors Anindita Septiarini, Anindita Ariani, Riska Arini Wijayanti Awang Harsa Kridalaksana Bahtiar , Andi Alfian Baskoro, Roby Gunawan Budi Gunawan Dyna Marisa Khairina Ervan, Muhamad Gusti Keyandi Fahrul Agus Fazari, Alawiyah Nur Firdha Ayu Selvyani, Firdha Ayu Firman Firman Gading, Fazri Rahmad Nor Haqqi, Kamalul Haviluddin Haviluddin Hendi Irman Hermadi Jundillah, Muhammad Labib Kamila, Vina Zahrotun Kurniati, Wendy Maman Turjaman Masyudi, Akhmad Nasution, Ali Napiah Nataniel Dengen Nuzulan, Alan Padmo Azam Masa, Amin Pramukti, Indra Cahya Pratiwi, Sinthya Ayu Puspita Puji Rahayu, Puspita Puji Puspitasari, Novianti Ramadiani Ramadiani - Ramadiani, Ramadiani - Rani, Famylia Puspa Rizqoni, Rizqoni S, Nadia Christin Borneo Sampe, Ferdinandus Santosa, Sonny Sembiring, Rinawati Sembiring, Wahyu Harry Saputra Septya Maharani, Septya Shapanara, Rhenaldi Octa Sri Nurdiati Wati, Masna Yudi Sukmono, Yudi Yunizar Fahmi Badron, Yunizar Fahmi Zainal Arifin